Spring2024_CSE572_Lab1_Intro_to_Python_Pandas.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sneberger/CSE572Lab1/blob/main/Spring2024_CSE572_Lab1_Intro_to_Python_Pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XVwFfRJY45Ez"
      },
      "source": [
        "# CSE 572: Lab 1\n",
        "\n",
        "This lab contains two modules:\n",
        "1.   Module 1: Introduction to Python\n",
        "2.   Module 2: Introduction to Numpy and Pandas\n",
        "\n",
        "At the end there is a practice exercise in which you will use some of the operations from Modules 1 and 2.\n",
        "\n",
        "To execute and make changes to this notebook, click File > Save a copy to save your own version in your Google Drive or Github. Read the step-by-step instructions below carefully. To execute the code, click on each cell below and press the SHIFT-ENTER keys simultaneously or by clicking the Play button.\n",
        "\n",
        "When you finish executing all code/exercises, save your notebook then download a copy (.ipynb file). Submit:\n",
        "\n",
        "1) .ipynb file\n",
        "\n",
        "2) link to your Colab notebook saved on your Google Drive (set the sharing options to share with anyone at ASU so the TA can view it). This should be added as a comment on your submission.\n",
        "\n",
        "3) pdf of executed notebook (In Colab, click File > Print > Save as PDF)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [],
      "source": []
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
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      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMsyODh_3IPd"
      },
      "source": [
        "# **PUT YOUR GROUP INFO HERE**\n",
        "\n",
        "| Group number | August Group XXX |            |\n",
        "|--------------|------------------|------------|\n",
        "| Member 1     | Michael Sneberger| 1000001544 |\n",
        "| Member 2     | Brandon Tran     | 1218701094 |\n",
        "| Member 3     | Lavany Jadhav    | 1228619251 |\n",
        "| Member 4     |                  |            |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJiv__lc4Hze"
      },
      "source": [
        "# Module 1: Introduction to Python\n",
        "\n",
        "Python is a high-level programming language with extensive libraries available to perform various data analysis tasks.\n",
        "The following tutorial contains examples of using various data types, functions, and library modules available in the standard Python library.\n",
        "\n",
        "We begin with some basic information about Python:\n",
        "1. Python is an interpreted language, unlike other high-level programming languages such as C or C++. You only need to submit your Python program to an interpreter for execution, without having to explicitly compile and link the code first.\n",
        "\n",
        "2. Python is a dynamically typed language, which means variable names are bound to their respective types during execution time. You do not have to explicitly declare the type of a variable before using it in the code unlike Java, C++, and other statically-typed languages.\n",
        "\n",
        "3. Instead of using braces '{' and '}', Python uses whitespace indentation to group together related statements in loops or other control-flow statements.\n",
        "\n",
        "4. Python uses the hash character ('#') to precede single-line comments. Triple-quoted strings (''') are commonly used to denote multi-line comments (even though it is not part of the standard Python language) or docstring of functions.\n",
        "\n",
        "5. Python uses pass by reference (instead of pass by value) when assigning a variable to another (e.g., a = b) or when passing an object as input argument to a function. Thus, any modification to the assigned variable or to the input argument within the function will affect the original object.  \n",
        "\n",
        "6. Python uses `None` to denote a null object (e.g., a = None). You do not have to terminate each statement with a terminating character (such as a semicolon) unlike other languages.\n",
        "\n",
        "7. You may access the variables or functions defined in another Python program file using the `import` command. This is analogous to the `import` command in Java or the `#include` command in C or C++."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IRxrl5lS5Ouv"
      },
      "outputs": [],
      "source": [
        "elieve the lab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLm6IlGs4Hzh"
      },
      "source": [
        "## 1.1 Elementary Data Types\n",
        "\n",
        "The standard Python library provides support for various elementary data types, including including integers, booleans, floating points, and strings. A summary of the data types is shown in the table below.\n",
        "\n",
        "| &nbsp;  |      Data Type    |   Example   |\n",
        "|:--------|:-----------------:|:------------|\n",
        "|Number   | Integer           | x = 4       |\n",
        "| &nbsp;  | Long integer      | x = 15L     |\n",
        "| &nbsp;  | Floating point    | x = 3.142   |\n",
        "| &nbsp;  | Boolean           | x = True    |\n",
        "|Text     | Character         | x = 'c'     |\n",
        "| &nbsp;  | String            | x = \"this\" or x = 'this' |"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "pcGGeqM_4Hzi"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4 <class 'int'>\n",
            "True <class 'bool'>\n",
            "3.7 <class 'float'>\n",
            "This is a string <class 'str'>\n"
          ]
        }
      ],
      "source": [
        "x = 4              # integer\n",
        "print(x, type(x))\n",
        "\n",
        "y = True           # boolean (True, False)\n",
        "print(y, type(y))\n",
        "\n",
        "z = 3.7            # floating point\n",
        "print(z, type(z))\n",
        "\n",
        "s = \"This is a string\"    # string\n",
        "print(s, type(s))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xMUnTNfK4Hzj"
      },
      "source": [
        "The following are some of the arithmetic operations available for manipulating integers and floating point numbers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "3J-wLIr34Hzj"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "18 8 12 6 18 2\n",
            "3.7 1.7000000000000002 1.2333333333333334 1.0 13.690000000000001\n",
            "3.7 13.690000000000001 4 3\n"
          ]
        }
      ],
      "source": [
        "x = 4            # integer\n",
        "x1 = x + 4       # addition\n",
        "x2 = x * 3       # multiplication\n",
        "x += 2           # equivalent to x = x + 2\n",
        "x3 = x\n",
        "x *= 3           # equivalent to x = x * 3\n",
        "x4 = x\n",
        "x5 = x % 4       # modulo (remainder) operator\n",
        "\n",
        "z = 3.7          # floating point number\n",
        "z1 = z - 2       # subtraction\n",
        "z2 = z / 3       # division\n",
        "z3 = z // 3      # integer division\n",
        "z4 = z ** 2      # square of z\n",
        "z5 = z4 ** 0.5   # square root\n",
        "z6 = pow(z,2)    # equivalent to square of z\n",
        "z7 = round(z)    # rounding z to its nearest integer\n",
        "z8 = int(z)      # type casting float to int\n",
        "\n",
        "print(x,x1,x2,x3,x4,x5)\n",
        "print(z,z1,z2,z3,z4)\n",
        "print(z5,z6,z7,z8)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PRtv2pIf4Hzk"
      },
      "source": [
        "The following are some of the functions provided by the math module for integers and floating point numbers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "WEtAf-WA4Hzk",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.0\n",
            "16.0\n",
            "54.598150033144236\n",
            "2.0\n",
            "4.0\n",
            "24\n",
            "1\n",
            "0\n",
            "0\n",
            "3.6739403974420594e-16\n",
            "0.9999999869751758\n",
            "True\n",
            "True\n"
          ]
        }
      ],
      "source": [
        "import math\n",
        "\n",
        "x = 4\n",
        "print(math.sqrt(x))      # sqrt(4) = 2\n",
        "print(math.pow(x,2))     # 4**2 = 16\n",
        "print(math.exp(x))       # exp(4) = 54.6\n",
        "print(math.log(x,2))     # log based 2  (default is natural logarithm)\n",
        "print(math.fabs(-4))     # absolute value\n",
        "print(math.factorial(x)) # 4! = 4 x 3 x 2 x 1 = 24\n",
        "\n",
        "z = 0.2\n",
        "print(math.ceil(z))      # ceiling function\n",
        "print(math.floor(z))     # floor function\n",
        "print(math.trunc(z))     # truncate function\n",
        "\n",
        "z = 3*math.pi            # math.pi = 3.141592653589793\n",
        "print(math.sin(z))       # sine function\n",
        "print(math.tanh(z))      # arctan function\n",
        "\n",
        "x = math.nan             # not a number\n",
        "print(math.isnan(x))\n",
        "\n",
        "x = math.inf             # infinity\n",
        "print(math.isinf(x))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "tBomt_Qp4Hzl"
      },
      "source": [
        "The following are some of the logical operations available for booleans"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "CgOsci3x4Hzl"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "False\n",
            "True\n",
            "True\n"
          ]
        }
      ],
      "source": [
        "y1 = True\n",
        "y2 = False\n",
        "\n",
        "print(y1 and y2)       # logical AND\n",
        "print(y1 or y2)        # logical OR\n",
        "print(y1 and not y2)   # logical NOT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hCytPHq64Hzm"
      },
      "source": [
        "The following are some of the operations and functions for manipulating strings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "lR_MkZdx4Hzm"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "his\n",
            "4\n",
            "Length of string is 4\n",
            "THIS\n",
            "this\n",
            "This\n",
            "This is another string\n",
            "That at a string\n",
            "8\n",
            "True\n",
            "True\n",
            "False\n",
            "This is a string too\n",
            "This This This \n"
          ]
        }
      ],
      "source": [
        "s1 = \"This\"\n",
        "\n",
        "print(s1[1:])                    # print last three characters\n",
        "print(len(s1))                               # get the string length\n",
        "print(\"Length of string is \" + str(len(s1))) # type casting int to str\n",
        "print(s1.upper())                            # convert to upper case\n",
        "print(s1.lower())                            # convert to lower case\n",
        "\n",
        "s2 = \"This is a string\"\n",
        "words = s2.split(' ')             # split the string into words\n",
        "print(words[0])\n",
        "print(s2.replace('a','another'))  # replace \"a\" with \"another\"\n",
        "print(s2.replace('is','at'))      # replace \"is\" with \"at\"\n",
        "print(s2.find(\"a\"))               # find the position of \"a\" in s2\n",
        "print(s1 in s2)                   # check if s1 is a substring of s2\n",
        "\n",
        "print(s1 == 'This')               # equality comparison\n",
        "print(s1 < 'That')                # inequality comparison\n",
        "print(s2 + \" too\")                # string concatenation\n",
        "print((s1 + \" \")* 3)              # replicate the string 3 times"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gprvq-Se4Hzn"
      },
      "source": [
        "## 1.2 Compound Data Types\n",
        "\n",
        "The following examples show how to create and manipulate a list object"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "6lu5cqWU4Hzn"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'list'>\n",
            "[1, 3, 5, 7, 9]\n",
            "[0, 2, 4, 6, 8]\n",
            "5\n",
            "[1, 3]\n",
            "[5, 7, 9]\n",
            "[7, 9]\n",
            "5\n",
            "25\n",
            "[1, 3, 5, 7, 9, 11]\n",
            "11\n",
            "[1, 3, 5, 7, 9]\n",
            "[1, 3, 5, 7, 9, 11, 13, 15]\n",
            "[1, 3, 5, 7, 9, 1, 3, 5, 7, 9, 1, 3, 5, 7, 9]\n",
            "[1, 3, 4, 5, 7, 9]\n",
            "[9, 7, 5, 4, 3, 1]\n"
          ]
        }
      ],
      "source": [
        "intlist = [1, 3, 5, 7, 9]\n",
        "print(type(intlist))\n",
        "print(intlist)\n",
        "intlist2 = list(range(0,10,2))   # range[startvalue, endvalue, stepsize]\n",
        "print(intlist2)\n",
        "\n",
        "print(intlist[2])                # get the third element of the list\n",
        "print(intlist[:2])               # get the first two elements\n",
        "print(intlist[2:])               # get the last three elements of the list\n",
        "print(intlist[-2:])              # get the last two elements of the list\n",
        "print(len(intlist))              # get the number of elements in the list\n",
        "print(sum(intlist))              # sums up elements of the list\n",
        "\n",
        "intlist.append(11)               # insert 11 to end of the list\n",
        "print(intlist)\n",
        "print(intlist.pop())             # remove last element of the list\n",
        "print(intlist)\n",
        "print(intlist + [11,13,15])      # concatenate two lists\n",
        "print(intlist * 3)               # replicate the list\n",
        "intlist.insert(2,4)              # insert item 4 at index 2\n",
        "print(intlist)\n",
        "intlist.sort(reverse=True)       # sort elements in descending order\n",
        "print(intlist)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "4rT-_Hxh4Hzn"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['this', 'is', 'a', 'list']\n",
            "<class 'list'>\n",
            "True\n",
            "a\n",
            "['this', 'is']\n",
            "['a', 'list']\n",
            "this is a list too\n",
            "['this', 'a', 'list', 'too']\n"
          ]
        }
      ],
      "source": [
        "mylist = ['this', 'is', 'a', 'list']\n",
        "print(mylist)\n",
        "print(type(mylist))\n",
        "\n",
        "print(\"list\" in mylist)          # check whether \"list\" is in mylist\n",
        "print(mylist[2])                 # show the 3rd element of the list\n",
        "print(mylist[:2])                # show the first two elements of the list\n",
        "print(mylist[2:])                # show the last two elements of the list\n",
        "mylist.append(\"too\")             # insert element to end of the list\n",
        "\n",
        "separator = \" \"\n",
        "print(separator.join(mylist))    # merge all elements of the list into a string\n",
        "\n",
        "mylist.remove(\"is\")              # remove element from list\n",
        "print(mylist)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CqE8ts0v4Hzo"
      },
      "source": [
        "The following examples show how to create and manipulate a dictionary object"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "YdHFePDe4Hzo"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'MI': 'Michigan', 'MN': 'Minnesota', 'TX': 'Texas', 'CA': 'California'}\n",
            "dict_keys(['MI', 'MN', 'TX', 'CA'])\n",
            "dict_values(['Michigan', 'Minnesota', 'Texas', 'California'])\n",
            "4\n",
            "Michigan\n",
            "False\n",
            "True\n"
          ]
        }
      ],
      "source": [
        "abbrev = {}\n",
        "abbrev['MI'] = \"Michigan\"\n",
        "abbrev['MN'] = \"Minnesota\"\n",
        "abbrev['TX'] = \"Texas\"\n",
        "abbrev['CA'] = \"California\"\n",
        "\n",
        "print(abbrev)\n",
        "print(abbrev.keys())            # get the keys of the dictionary\n",
        "print(abbrev.values())          # get the values of the dictionary\n",
        "print(len(abbrev))              # get number of key-value pairs\n",
        "\n",
        "print(abbrev.get('MI'))\n",
        "print(\"FL\" in abbrev)\n",
        "print(\"CA\" in abbrev)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "tL66TQzh63VU"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'apples': 3, 'oranges': 4, 'bananas': 2, 'cherries': 10}\n",
            "['apples', 'bananas', 'cherries', 'oranges']\n",
            "[('apples', 3), ('bananas', 2), ('cherries', 10), ('oranges', 4)]\n",
            "[('bananas', 2), ('apples', 3), ('oranges', 4), ('cherries', 10)]\n"
          ]
        }
      ],
      "source": [
        "keys = ['apples', 'oranges', 'bananas', 'cherries']\n",
        "values = [3, 4, 2, 10]\n",
        "fruits = dict(zip(keys, values))\n",
        "print(fruits)\n",
        "print(sorted(fruits))     # sort keys of dictionary\n",
        "\n",
        "from operator import itemgetter\n",
        "print(sorted(fruits.items(), key=itemgetter(0)))    # sort by key of dictionary\n",
        "print(sorted(fruits.items(), key=itemgetter(1)))    # sort by value of dictionary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YLe3-CGH4Hzo"
      },
      "source": [
        "The following examples show how to create and manipulate a tuple object. Unlike a list, a tuple object is immutable, i.e., they cannot be modified after creation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "O8FoMBO34Hzo"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "('MI', 'Michigan', 'Lansing')\n",
            "('Michigan', 'Lansing')\n",
            "[('MI', 'Michigan', 'Lansing'), ('CA', 'California', 'Sacramento'), ('TX', 'Texas', 'Austin')]\n",
            "('TX', 'Texas', 'Austin')\n",
            "('TX', 'Texas', 'Austin')\n",
            "('Texas', 'Austin')\n",
            "[('TX', 'Texas', 'Austin'), ('MI', 'Michigan', 'Lansing'), ('CA', 'California', 'Sacramento')]\n"
          ]
        }
      ],
      "source": [
        "MItuple = ('MI', 'Michigan', 'Lansing')\n",
        "CAtuple = ('CA', 'California', 'Sacramento')\n",
        "TXtuple = ('TX', 'Texas', 'Austin')\n",
        "\n",
        "print(MItuple)\n",
        "print(MItuple[1:])\n",
        "\n",
        "states = [MItuple, CAtuple, TXtuple]    # this will create a list of tuples\n",
        "print(states)\n",
        "print(states[2]) # print the third tuple in the list\n",
        "print(states[2][:]) # print all the values in the third tuple\n",
        "print(states[2][1:]) # print the last two values in the third tuple\n",
        "\n",
        "states.sort(key=lambda state: state[2])  # sort the states by their capital cities (last value in list)\n",
        "print(states)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ye5okI-s4Hzp"
      },
      "source": [
        "## 1.3 Control Flow Statements\n",
        "\n",
        "Similar to other programming languages, the control flow statements in Python include if, for, and while statements. Examples on how to use these statements are shown below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "2bH2koES4Hzp"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x = 10 is even\n",
            "x = 10 is positive\n"
          ]
        }
      ],
      "source": [
        "# using if-else statement\n",
        "\n",
        "x = 10\n",
        "\n",
        "if x % 2 == 0:\n",
        "    print(\"x =\", x, \"is even\")\n",
        "else:\n",
        "    print(\"x =\", x, \"is odd\")\n",
        "\n",
        "if x > 0:\n",
        "    print(\"x =\", x, \"is positive\")\n",
        "elif x < 0:\n",
        "    print(\"x =\", x, \"is negative\")\n",
        "else:\n",
        "    print(\"x =\", x, \"is neither positive nor negative\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "uAAsYQ-R4Hzp"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "that\n",
            "at\n",
            "a\n",
            "latt\n",
            "[4, 2, 1, 4]\n"
          ]
        }
      ],
      "source": [
        "# using for loop with a list\n",
        "\n",
        "mylist = ['this', 'is', 'a', 'list']\n",
        "for word in mylist:\n",
        "    print(word.replace(\"is\", \"at\"))\n",
        "\n",
        "mylist2 = [len(word) for word in mylist]   # number of characters in each word\n",
        "print(mylist2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "nkrnBoTk7Xwz"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Austin', 'Lansing', 'Sacramento']\n"
          ]
        }
      ],
      "source": [
        "# using for loop with list of tuples\n",
        "\n",
        "states = [('MI', 'Michigan', 'Lansing'),('CA', 'California', 'Sacramento'),\n",
        "          ('TX', 'Texas', 'Austin')]\n",
        "\n",
        "sorted_capitals = [state[2] for state in states]\n",
        "sorted_capitals.sort()\n",
        "print(sorted_capitals)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "Ksn5nPTc7X3C"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['apples', 'oranges', 'bananas', 'cherries']\n"
          ]
        }
      ],
      "source": [
        "# using for loop with dictionary\n",
        "\n",
        "fruits = {'apples': 3, 'oranges': 4, 'bananas': 2, 'cherries': 10}\n",
        "fruitnames = [k for (k,v) in fruits.items()]\n",
        "print(fruitnames)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "QQy8IAZ64Hzp"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
            "First non-negative number: 0\n"
          ]
        }
      ],
      "source": [
        "# using while loop\n",
        "\n",
        "mylist = list(range(-10,10))\n",
        "print(mylist)\n",
        "\n",
        "i = 0\n",
        "while (mylist[i] < 0):\n",
        "    i = i + 1\n",
        "\n",
        "print(\"First non-negative number:\", mylist[i])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaz47WDf4Hzp"
      },
      "source": [
        "## 1.4 User-Defined Functions\n",
        "\n",
        "You can create your own functions in Python, which can be named or unnamed. Unnamed functions are defined using the lambda keyword as shown in the previous example for sorting a list of tuples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "OBiu0dA44Hzq"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "11\n"
          ]
        }
      ],
      "source": [
        "myfunc = lambda x: 3*x**2 - 2*x + 3      # example of an unnamed quadratic function\n",
        "\n",
        "print(myfunc(2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "03osRRxD4Hzq"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[-11, 12, 23, 45, 71]\n"
          ]
        }
      ],
      "source": [
        "import math\n",
        "\n",
        "# The following function will discard missing values from a list\n",
        "def discard(inlist, sortFlag=False):    # default value for sortFlag is False\n",
        "    outlist = []\n",
        "    for item in inlist:\n",
        "        if not math.isnan(item):\n",
        "            outlist.append(item)\n",
        "\n",
        "    if sortFlag:\n",
        "        outlist.sort()\n",
        "    return outlist\n",
        "\n",
        "mylist = [12, math.nan, 23, -11, 45, math.nan, 71]\n",
        "\n",
        "print(discard(mylist,True))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O16R0VrY4Hzq"
      },
      "source": [
        "## 1.5 File I/O\n",
        "\n",
        "You can read and write data from a list or other objects to a file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "9eXaUnY_4Hzq"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "State= Michigan ( MI ) Capital: Lansing\n",
            "\n",
            "State= California ( CA ) Capital: Sacramento\n",
            "\n",
            "State= Texas ( TX ) Capital: Austin\n",
            "\n",
            "State= Minnesota ( MN ) Capital: St Paul\n"
          ]
        }
      ],
      "source": [
        "states = [('MI', 'Michigan', 'Lansing'),('CA', 'California', 'Sacramento'),\n",
        "          ('TX', 'Texas', 'Austin'), ('MN', 'Minnesota', 'St Paul')]\n",
        "\n",
        "with open('states.txt', 'w') as f:\n",
        "    f.write('\\n'.join('%s,%s,%s' % state for state in states))\n",
        "\n",
        "with open('states.txt', 'r') as f:\n",
        "    for line in f:\n",
        "        fields = line.split(sep=',')    # split each line into its respective fields\n",
        "        print('State=',fields[1],'(',fields[0],')','Capital:', fields[2])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tiJ2qIFs8eFL"
      },
      "source": [
        "# Module 2: Introduction to Numpy and Pandas\n",
        "\n",
        "The following tutorial contains examples of using the numpy and pandas library modules."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPqEga6N8eFO"
      },
      "source": [
        "## 2.1 Introduction to Numpy\n",
        "\n",
        "Numpy, which stands for numerical Python, is a Python library package to support numerical computations. The basic data structure in numpy is a multi-dimensional array object called ndarray. Numpy provides a suite of functions that can efficiently manipulate elements of the ndarray.\n",
        "\n",
        "### 2.1.1 Creating ndarray\n",
        "\n",
        "An ndarray can be created from a list or a tuple object as shown in the examples below. It is possible to create a 1-dimensional or multi-dimensional array from the list objects as well as tuples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "txAiuLoK8eFO"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1. 2. 3. 4. 5.]\n",
            "#Dimensions = 1\n",
            "Dimension = (5,)\n",
            "Size = 5\n",
            "Array type = float64 \n",
            "\n",
            "[[1 2]\n",
            " [3 4]\n",
            " [5 6]\n",
            " [7 8]]\n",
            "#Dimensions = 2\n",
            "Dimension = (4, 2)\n",
            "Size = 8\n",
            "Array type = int32 \n",
            "\n",
            "[['1' 'a' '3.0']\n",
            " ['2' 'b' '3.5']]\n",
            "#Dimensions = 2\n",
            "Dimension = (2, 3)\n",
            "Size = 6\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "oneDim = np.array([1.0,2,3,4,5])   # a 1-dimensional array (vector)\n",
        "print(oneDim)\n",
        "print(\"#Dimensions =\", oneDim.ndim)\n",
        "print(\"Dimension =\", oneDim.shape)\n",
        "print(\"Size =\", oneDim.size)\n",
        "print(\"Array type =\", oneDim.dtype, '\\n')\n",
        "\n",
        "twoDim = np.array([[1,2],[3,4],[5,6],[7,8]])  # a two-dimensional array (matrix)\n",
        "print(twoDim)\n",
        "print(\"#Dimensions =\", twoDim.ndim)\n",
        "print(\"Dimension =\", twoDim.shape)\n",
        "print(\"Size =\", twoDim.size)\n",
        "print(\"Array type =\", twoDim.dtype, '\\n')\n",
        "\n",
        "arrFromTuple = np.array([(1,'a',3.0),(2,'b',3.5)])  # create ndarray from tuple\n",
        "print(arrFromTuple)\n",
        "print(\"#Dimensions =\", arrFromTuple.ndim)\n",
        "print(\"Dimension =\", arrFromTuple.shape)\n",
        "print(\"Size =\", arrFromTuple.size)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rC5i_a1O8eFP"
      },
      "source": [
        "There are also built-in functions available in numpy to create the ndarrays."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "CSkDAuLt8eFQ"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Array of random numbers from a uniform distribution\n",
            "[0.08786684 0.10399641 0.33903804 0.29601642 0.04328248]\n",
            "\n",
            "Array of random numbers from a normal distribution\n",
            "[ 0.1722209   0.68513369 -0.49921122  1.86681529  2.2160792 ]\n",
            "\n",
            "Array of integers between -10 and 10, with step size of 2\n",
            "[-10  -8  -6  -4  -2   0   2   4   6   8]\n",
            "\n",
            "2-dimensional array of integers from 0 to 11\n",
            "[[ 0  1  2  3]\n",
            " [ 4  5  6  7]\n",
            " [ 8  9 10 11]]\n",
            "\n",
            "Array of values between 0 and 1, split into 10 equally spaced values\n",
            "[0.         0.11111111 0.22222222 0.33333333 0.44444444 0.55555556\n",
            " 0.66666667 0.77777778 0.88888889 1.        ]\n",
            "\n",
            "Array of values from 10^-3 to 10^3\n",
            "[1.e-03 1.e-02 1.e-01 1.e+00 1.e+01 1.e+02 1.e+03]\n"
          ]
        }
      ],
      "source": [
        "print('Array of random numbers from a uniform distribution')\n",
        "print(np.random.rand(5))      # random numbers from a uniform distribution between [0,1]\n",
        "\n",
        "print('\\nArray of random numbers from a normal distribution')\n",
        "print(np.random.randn(5))     # random numbers from a normal distribution\n",
        "\n",
        "print('\\nArray of integers between -10 and 10, with step size of 2')\n",
        "print(np.arange(-10,10,2))    # similar to range, but returns ndarray instead of list\n",
        "\n",
        "print('\\n2-dimensional array of integers from 0 to 11')\n",
        "print(np.arange(12).reshape(3,4))  # reshape to a matrix\n",
        "\n",
        "print('\\nArray of values between 0 and 1, split into 10 equally spaced values')\n",
        "print(np.linspace(0,1,10))    # split interval [0,1] into 10 equally separated values\n",
        "\n",
        "print('\\nArray of values from 10^-3 to 10^3')\n",
        "print(np.logspace(-3,3,7))    # create ndarray with values from 10^-3 to 10^3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "0iwVlNoP8eFQ"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A 2 x 3 matrix of zeros\n",
            "[[0. 0. 0.]\n",
            " [0. 0. 0.]]\n",
            "\n",
            "A 3 x 2 matrix of ones\n",
            "[[1. 1.]\n",
            " [1. 1.]\n",
            " [1. 1.]]\n",
            "\n",
            "A 3 x 3 identity matrix\n",
            "[[1. 0. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 0. 1.]]\n"
          ]
        }
      ],
      "source": [
        "print('A 2 x 3 matrix of zeros')\n",
        "print(np.zeros((2,3)))        # a matrix of zeros\n",
        "\n",
        "print('\\nA 3 x 2 matrix of ones')\n",
        "print(np.ones((3,2)))         # a matrix of ones\n",
        "\n",
        "print('\\nA 3 x 3 identity matrix')\n",
        "print(np.eye(3))              # a 3 x 3 identity matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0POKFGJS8eFR"
      },
      "source": [
        "## 2.1.2 Element-wise Operations\n",
        "\n",
        "You can apply standard operators such as addition and multiplication on each element of the ndarray."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "rNrS-RtY8eFR"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x = [1 2 3 4 5]\n",
            "x + 1 = [2 3 4 5 6]\n",
            "x - 1 = [0 1 2 3 4]\n",
            "x * 2 = [ 2  4  6  8 10]\n",
            "x // 2 = [0 1 1 2 2]\n",
            "x ** 2 = [ 1  4  9 16 25]\n",
            "x % 2 = [1 0 1 0 1]\n",
            "1 / x = [1.         0.5        0.33333333 0.25       0.2       ]\n"
          ]
        }
      ],
      "source": [
        "x = np.array([1,2,3,4,5])\n",
        "\n",
        "print('x =', x)\n",
        "print('x + 1 =', x + 1)      # addition\n",
        "print('x - 1 =', x - 1)      # subtraction\n",
        "print('x * 2 =', x * 2)      # multiplication\n",
        "print('x // 2 =', x // 2)     # integer division\n",
        "print('x ** 2 =', x ** 2)     # square\n",
        "print('x % 2 =', x % 2)      # modulo\n",
        "print('1 / x =', 1 / x)      # division"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "oL3nyflj8eFS"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x = [ 2  4  6  8 10]\n",
            "y = [1 2 3 4 5]\n",
            "x + y = [ 3  6  9 12 15]\n",
            "x - y = [1 2 3 4 5]\n",
            "x * y = [ 2  8 18 32 50]\n",
            "x / y = [2. 2. 2. 2. 2.]\n",
            "x // y = [2 2 2 2 2]\n",
            "x ** y = [     2     16    216   4096 100000]\n"
          ]
        }
      ],
      "source": [
        "x = np.array([2,4,6,8,10])\n",
        "y = np.array([1,2,3,4,5])\n",
        "\n",
        "print('x =', x)\n",
        "print('y =', y)\n",
        "print('x + y =', x + y)      # element-wise addition\n",
        "print('x - y =', x - y)      # element-wise subtraction\n",
        "print('x * y =', x * y)      # element-wise multiplication\n",
        "print('x / y =', x / y)      # element-wise division\n",
        "print('x // y =', x // y)    # element-wise integer division\n",
        "print('x ** y =', x ** y)    # element-wise exponentiation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jUQAoEk88eFS"
      },
      "source": [
        "## 2.1.3 Indexing and Slicing\n",
        "\n",
        "There are various ways to select a subset of elements within a numpy array. Assigning a numpy array (or a subset of its elements) to another variable will simply pass a reference to the array instead of copying its values. To make a copy of an ndarray, you need to explicitly call the .copy() function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "fFmQbMCR8eFT"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Before: x = [-5 -4 -3 -2 -1  0  1  2  3  4]\n",
            "        y = [-2 -1]\n",
            "After : y = [1000 1000]\n",
            "        x = [  -5   -4   -3 1000 1000    0    1    2    3    4] \n",
            "\n",
            "Before: x = [  -5   -4   -3 1000 1000    0    1    2    3    4]\n",
            "        z = [1000 1000]\n",
            "After : z = [500 500]\n",
            "        x = [  -5   -4   -3 1000 1000    0    1    2    3    4]\n"
          ]
        }
      ],
      "source": [
        "x = np.arange(-5,5)\n",
        "print('Before: x =', x)\n",
        "\n",
        "y = x[3:5]     # y is a slice, i.e., pointer to a subarray in x\n",
        "print('        y =', y)\n",
        "y[:] = 1000    # modifying the value of y will change x\n",
        "print('After : y =', y)\n",
        "print('        x =', x, '\\n')\n",
        "\n",
        "z = x[3:5].copy()   # makes a copy of the subarray\n",
        "print('Before: x =', x)\n",
        "print('        z =', z)\n",
        "z[:] = 500          # modifying the value of z will not affect x\n",
        "print('After : z =', z)\n",
        "print('        x =', x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4TfP_zqF8eFT"
      },
      "source": [
        "There are many ways to access elements of an ndarray. The following example illustrates the difference between indexing elements of a list and elements of ndarray."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "GITA1NTE8eFU"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "my2dlist = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]\n",
            "my2dlist[2] = [9, 10, 11, 12]\n",
            "my2dlist[:][2] = [9, 10, 11, 12]\n",
            "\n",
            "my2darr =\n",
            " [[ 1  2  3  4]\n",
            " [ 5  6  7  8]\n",
            " [ 9 10 11 12]]\n",
            "my2darr[2][:] = [ 9 10 11 12]\n",
            "my2darr[2,:] = [ 9 10 11 12]\n",
            "my2darr[:][2] = [ 9 10 11 12]\n",
            "my2darr[:,2] = [ 3  7 11]\n",
            "my2darr[:2,2:] =\n",
            " [[3 4]\n",
            " [7 8]]\n"
          ]
        }
      ],
      "source": [
        "my2dlist = [[1,2,3,4],[5,6,7,8],[9,10,11,12]]  # a 2-dim list\n",
        "print('my2dlist =', my2dlist)\n",
        "print('my2dlist[2] =', my2dlist[2])            # access the third sublist\n",
        "print('my2dlist[:][2] =', my2dlist[:][2])      # can't access third element of each sublist\n",
        "# print('my2dlist[:,2] =', my2dlist[:,2])      # invalid way to access sublist, will cause syntax error\n",
        "\n",
        "my2darr = np.array(my2dlist)\n",
        "print('\\nmy2darr =\\n', my2darr)\n",
        "\n",
        "print('my2darr[2][:] =', my2darr[2][:])      # access the third row\n",
        "print('my2darr[2,:] =', my2darr[2,:])        # access the third row\n",
        "print('my2darr[:][2] =', my2darr[:][2])      # access the third row (similar to 2d list)\n",
        "print('my2darr[:,2] =', my2darr[:,2])        # access the third column\n",
        "print('my2darr[:2,2:] =\\n', my2darr[:2,2:])     # access the first two rows & last two columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ckhE9Ac8eFU"
      },
      "source": [
        "Numpy arrays also support boolean indexing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "VBnc3VUW8eFU"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "my2darr =\n",
            " [[ 1  2  3  4]\n",
            " [ 5  6  7  8]\n",
            " [ 9 10 11 12]]\n",
            "\n",
            "my2darr[my2darr % 3 == 0] = [ 3  6  9 12]\n",
            "my2darr[2:, my2darr[2,:] % 3 == 0] = [[ 9 12]]\n"
          ]
        }
      ],
      "source": [
        "my2darr = np.arange(1,13,1).reshape(3,4)\n",
        "print('my2darr =\\n', my2darr)\n",
        "\n",
        "divBy3 = my2darr[my2darr % 3 == 0]\n",
        "print('\\nmy2darr[my2darr % 3 == 0] =', divBy3)            # returns all the elements divisible by 3 in an ndarray\n",
        "\n",
        "divBy3LastRow = my2darr[2:, my2darr[2,:] % 3 == 0]\n",
        "print('my2darr[2:, my2darr[2,:] % 3 == 0] =', divBy3LastRow)    # returns elements in the last row divisible by 3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "88JbejrE8eFU"
      },
      "source": [
        "More indexing examples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "0H-GSGri8eFV"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "my2darr =\n",
            " [[ 1  2  3]\n",
            " [ 4  5  6]\n",
            " [ 7  8  9]\n",
            " [10 11 12]]\n",
            "indices = [2, 1, 0, 3] \n",
            "\n",
            "my2darr[indices,:] =\n",
            " [[ 7  8  9]\n",
            " [ 4  5  6]\n",
            " [ 1  2  3]\n",
            " [10 11 12]]\n",
            "\n",
            "rowIndex = [0, 0, 1, 2, 3]\n",
            "columnIndex = [0, 2, 0, 1, 2] \n",
            "\n",
            "my2darr[rowIndex,columnIndex] = [ 1  3  4  8 12]\n"
          ]
        }
      ],
      "source": [
        "my2darr = np.arange(1,13,1).reshape(4,3)\n",
        "print('my2darr =\\n', my2darr)\n",
        "\n",
        "indices = [2,1,0,3]    # selected row indices\n",
        "print('indices =', indices, '\\n')\n",
        "print('my2darr[indices,:] =\\n', my2darr[indices,:])  # this will shuffle the rows of my2darr\n",
        "\n",
        "rowIndex = [0,0,1,2,3]     # row index into my2darr\n",
        "print('\\nrowIndex =', rowIndex)\n",
        "columnIndex = [0,2,0,1,2]  # column index into my2darr\n",
        "print('columnIndex =', columnIndex, '\\n')\n",
        "print('my2darr[rowIndex,columnIndex] =', my2darr[rowIndex,columnIndex])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9G--4dS8eFV"
      },
      "source": [
        "## 2.1.4 Numpy Arithmetic and Statistical Functions\n",
        "\n",
        "Numpy provides many built-in mathematical functions available for manipulating elements of an ndarray."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "lgLZk7n28eFV"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "y = [-1.4  0.4 -3.2  2.5  3.4] \n",
            "\n",
            "np.abs(y) = [1.4 0.4 3.2 2.5 3.4]\n",
            "np.sqrt(abs(y)) = [1.18321596 0.63245553 1.78885438 1.58113883 1.84390889]\n",
            "np.sign(y) = [-1.  1. -1.  1.  1.]\n",
            "np.exp(y) = [ 0.24659696  1.4918247   0.0407622  12.18249396 29.96410005]\n",
            "np.sort(y) = [-3.2 -1.4  0.4  2.5  3.4]\n"
          ]
        }
      ],
      "source": [
        "y = np.array([-1.4, 0.4, -3.2, 2.5, 3.4])\n",
        "print('y =', y, '\\n')\n",
        "\n",
        "print('np.abs(y) =', np.abs(y))                # convert to absolute values\n",
        "print('np.sqrt(abs(y)) =', np.sqrt(abs(y)))    # apply square root to each element\n",
        "print('np.sign(y) =', np.sign(y))              # get the sign of each element\n",
        "print('np.exp(y) =', np.exp(y))                # apply exponentiation\n",
        "print('np.sort(y) =', np.sort(y))              # sort array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "Ngz1X5008eFV"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x = [-2 -1  0  1  2]\n",
            "y = [-0.67342145 -0.41580666 -0.05657406 -0.31976087 -1.00110262] \n",
            "\n",
            "np.add(x,y) = [-2.67342145 -1.41580666 -0.05657406  0.68023913  0.99889738]\n",
            "np.subtract(x,y) = [-1.32657855 -0.58419334  0.05657406  1.31976087  3.00110262]\n",
            "np.multiply(x,y) = [ 1.34684291  0.41580666 -0.         -0.31976087 -2.00220523]\n",
            "np.divide(x,y) = [ 2.96990835  2.40496391 -0.         -3.12733703 -1.9977972 ]\n",
            "np.maximum(x,y) = [-0.67342145 -0.41580666  0.          1.          2.        ]\n"
          ]
        }
      ],
      "source": [
        "x = np.arange(-2,3)\n",
        "y = np.random.randn(5)\n",
        "print('x =', x)\n",
        "print('y =', y, '\\n')\n",
        "\n",
        "print('np.add(x,y) =', np.add(x,y))                # element-wise addition       x + y\n",
        "print('np.subtract(x,y) =', np.subtract(x,y))      # element-wise subtraction    x - y\n",
        "print('np.multiply(x,y) =', np.multiply(x,y))      # element-wise multiplication x * y\n",
        "print('np.divide(x,y) =', np.divide(x,y))          # element-wise division       x / y\n",
        "print('np.maximum(x,y) =', np.maximum(x,y))        # element-wise maximum        max(x,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "NBKUfnaI8eFW"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "y = [-3.2 -1.4  0.4  2.5  3.4] \n",
            "\n",
            "Min = -3.2\n",
            "Max = 3.4\n",
            "Average = 0.34000000000000014\n",
            "Std deviation = 2.432776191925595\n",
            "Sum = 1.7000000000000006\n"
          ]
        }
      ],
      "source": [
        "y = np.array([-3.2, -1.4, 0.4, 2.5, 3.4])\n",
        "print('y =', y, '\\n')\n",
        "\n",
        "print(\"Min =\", np.min(y))             # min\n",
        "print(\"Max =\", np.max(y))             # max\n",
        "print(\"Average =\", np.mean(y))        # mean/average\n",
        "print(\"Std deviation =\", np.std(y))   # standard deviation\n",
        "print(\"Sum =\", np.sum(y))             # sum"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yu9ActUA8eFW"
      },
      "source": [
        "## 2.1.5 Numpy linear algebra\n",
        "\n",
        "Numpy provides many functions to support linear algebra operations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "iqHu02EC8eFW"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "X =\n",
            " [[-0.38826136 -0.80463824  0.12841699]\n",
            " [-0.60933694  1.00734776 -0.16794406]] \n",
            "\n",
            "Transpose of X, X.T =\n",
            " [[-0.38826136 -0.60933694]\n",
            " [-0.80463824  1.00734776]\n",
            " [ 0.12841699 -0.16794406]] \n",
            "\n",
            "y = [ 0.467587    1.19050059 -0.3809059 ] \n",
            "\n",
            "Matrix-vector multiplication\n",
            "X.dot(y) =\n",
            " [-1.18838305  0.97830095] \n",
            "\n",
            "Matrix-matrix product\n",
            "X.dot(X.T) = [[ 0.8146805  -0.59553542]\n",
            " [-0.59553542  1.41424622]]\n",
            "\n",
            "X.T.dot(X) =\n",
            " [[ 0.52203838 -0.30140426  0.05247516]\n",
            " [-0.30140426  1.66219221 -0.27250729]\n",
            " [ 0.05247516 -0.27250729  0.04469613]]\n"
          ]
        }
      ],
      "source": [
        "X = np.random.randn(2,3)                         # create a 2 x 3 random matrix\n",
        "print('X =\\n', X, '\\n')\n",
        "print('Transpose of X, X.T =\\n', X.T, '\\n')      # matrix transpose operation X^T\n",
        "\n",
        "y = np.random.randn(3) # random vector\n",
        "print('y =', y, '\\n')\n",
        "\n",
        "print('Matrix-vector multiplication')\n",
        "print('X.dot(y) =\\n', X.dot(y), '\\n')            # matrix-vector multiplication  X * y\n",
        "\n",
        "print('Matrix-matrix product')\n",
        "print('X.dot(X.T) =', X.dot(X.T))        # matrix-matrix multiplication  X * X^T\n",
        "print('\\nX.T.dot(X) =\\n', X.T.dot(X))      # matrix-matrix multiplication  X^T * X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "MJj0VYMI8eFW"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "X =\n",
            " [[ 0.89645143  0.39617673  0.59012705]\n",
            " [ 1.33831111  0.84665381  1.8080863 ]\n",
            " [ 0.25760215  0.85679726 -0.33216652]\n",
            " [-0.16539312  1.06951777  0.19814967]\n",
            " [-0.76980014  0.72610955  0.4774823 ]] \n",
            "\n",
            "C = X.T.dot(X) =\n",
            " [[3.2810078  0.97310209 2.46289687]\n",
            " [0.97310209 3.27898356 2.03864745]\n",
            " [2.46289687 2.03864745 3.99501324]] \n",
            "\n",
            "Inverse of C = np.linalg.inv(C)\n",
            " [[ 0.57914117  0.0733954  -0.39448989]\n",
            " [ 0.0733954   0.45599615 -0.27794168]\n",
            " [-0.39448989 -0.27794168  0.63534533]] \n",
            "\n",
            "Determinant of C = np.linalg.det(C) = 15.442693053409553\n",
            "Eigenvalues of C =\n",
            " [7.31843088 2.33154873 0.90502498]\n",
            "Eigenvectors of C =\n",
            " [[-0.538332   -0.60222777 -0.58950858]\n",
            " [-0.4794565   0.79414226 -0.37344282]\n",
            " [-0.69305131 -0.08160751  0.71625421]]\n"
          ]
        }
      ],
      "source": [
        "X = np.random.randn(5,3)\n",
        "print('X =\\n', X, '\\n')\n",
        "\n",
        "C = X.T.dot(X)               # C = X^T * X is a square matrix\n",
        "print('C = X.T.dot(X) =\\n', C, '\\n')\n",
        "\n",
        "invC = np.linalg.inv(C)      # inverse of a square matrix\n",
        "print('Inverse of C = np.linalg.inv(C)\\n', invC, '\\n')\n",
        "\n",
        "detC = np.linalg.det(C)      # determinant of a square matrix\n",
        "print('Determinant of C = np.linalg.det(C) =', detC)\n",
        "\n",
        "S, U = np.linalg.eig(C)      # eigenvalue S and eigenvector U of a square matrix\n",
        "print('Eigenvalues of C =\\n', S)\n",
        "print('Eigenvectors of C =\\n', U)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-n2JvvBu8eFW"
      },
      "source": [
        "## 2.2 Introduction to Pandas\n",
        "\n",
        "Pandas provide two convenient data structures for storing and manipulating data: Series and DataFrame. A Series is similar to a one-dimensional array whereas a DataFrame is a tabular representation akin to a spreadsheet table.  \n",
        "\n",
        "### 2.2.1 Series\n",
        "\n",
        "A Series object consists of a one-dimensional array of values, whose elements can be referenced using an index array. A Series object can be created from a list, a numpy array, or a Python dictionary. You can apply most of the numpy functions on the Series object."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "h-lkdnsC8eFX"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Series, s =\n",
            " 0    3.1\n",
            "1    2.4\n",
            "2   -1.7\n",
            "3    0.2\n",
            "4   -2.9\n",
            "5    4.5\n",
            "dtype: float64 \n",
            "\n",
            "s.values = [ 3.1  2.4 -1.7  0.2 -2.9  4.5]\n",
            "s.index = RangeIndex(start=0, stop=6, step=1)\n",
            "s.dtype = float64\n"
          ]
        }
      ],
      "source": [
        "from pandas import Series\n",
        "\n",
        "s = Series([3.1, 2.4, -1.7, 0.2, -2.9, 4.5])   # creating a series from a list\n",
        "print('Series, s =\\n', s, '\\n')\n",
        "\n",
        "print('s.values =', s.values)     # display values of the Series\n",
        "print('s.index =', s.index)       # display indices of the Series\n",
        "print('s.dtype =', s.dtype)       # display the element type of the Series"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "TN7Gd1fG8eFX"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Series s2 =\n",
            " 0    0.468945\n",
            "1    1.583856\n",
            "2    2.365801\n",
            "3    1.374661\n",
            "4    0.236459\n",
            "5    1.981072\n",
            "dtype: float64 \n",
            "\n",
            "s2.values = [0.46894516 1.58385591 2.36580129 1.37466057 0.23645882 1.98107161]\n",
            "s2.index = RangeIndex(start=0, stop=6, step=1)\n",
            "s2.dtype = float64\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "s2 = Series(np.random.randn(6))   # creating a series from a numpy ndarray\n",
        "print('Series s2 =\\n', s2, '\\n')\n",
        "print('s2.values =', s2.values)   # display values of the Series\n",
        "print('s2.index =', s2.index)     # display indices of the Series\n",
        "print('s2.dtype =', s2.dtype)     # display the element type of the Series"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "6ZjSVEUT8eFX"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Series s3 =\n",
            " Jan 1    1.2\n",
            "Jan 2    2.5\n",
            "Jan 3   -2.2\n",
            "Jan 4    3.1\n",
            "Jan 5   -0.8\n",
            "Jan 6   -3.2\n",
            "dtype: float64 \n",
            "\n",
            "s3.values = [ 1.2  2.5 -2.2  3.1 -0.8 -3.2]\n",
            "s3.index = Index(['Jan 1', 'Jan 2', 'Jan 3', 'Jan 4', 'Jan 5', 'Jan 6'], dtype='object')\n",
            "s3.dtype = float64\n"
          ]
        }
      ],
      "source": [
        "s3 = Series([1.2,2.5,-2.2,3.1,-0.8,-3.2],\n",
        "            index = ['Jan 1','Jan 2','Jan 3','Jan 4','Jan 5','Jan 6',])\n",
        "print('Series s3 =\\n', s3, '\\n')\n",
        "print('s3.values =', s3.values)   # display values of the Series\n",
        "print('s3.index =', s3.index)     # display indices of the Series\n",
        "print('s3.dtype =', s3.dtype)     # display the element type of the Series"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "tUvgI8jp8eFX"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Series s4 =\n",
            " MI       Lansing\n",
            "CA    Sacramento\n",
            "TX        Austin\n",
            "MN       St Paul\n",
            "dtype: object \n",
            "\n",
            "s4.values = ['Lansing' 'Sacramento' 'Austin' 'St Paul']\n",
            "s4.index= Index(['MI', 'CA', 'TX', 'MN'], dtype='object')\n",
            "s4.dtype = object\n"
          ]
        }
      ],
      "source": [
        "capitals = {'MI': 'Lansing', 'CA': 'Sacramento', 'TX': 'Austin', 'MN': 'St Paul'}\n",
        "\n",
        "s4 = Series(capitals)   # creating a series from dictionary object\n",
        "print('Series s4 =\\n', s4, '\\n')\n",
        "print('s4.values =', s4.values)   # display values of the Series\n",
        "print('s4.index=', s4.index)      # display indices of the Series\n",
        "print('s4.dtype =', s4.dtype)     # display the element type of the Series"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "Enbb-W3h8eFX"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "s3 =\n",
            " Jan 1    1.2\n",
            "Jan 2    2.5\n",
            "Jan 3   -2.2\n",
            "Jan 4    3.1\n",
            "Jan 5   -0.8\n",
            "Jan 6   -3.2\n",
            "dtype: float64 \n",
            "\n",
            "s3[2]= -2.2\n",
            "s3['Jan 3']= -2.2\n",
            "\n",
            "s3[1:3]=\n",
            "Jan 2    2.5\n",
            "Jan 3   -2.2\n",
            "dtype: float64\n",
            "\n",
            "s3.iloc([1:3])=\n",
            "Jan 2    2.5\n",
            "Jan 3   -2.2\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "s3 = Series([1.2,2.5,-2.2,3.1,-0.8,-3.2],\n",
        "            index = ['Jan 1','Jan 2','Jan 3','Jan 4','Jan 5','Jan 6',])\n",
        "print('s3 =\\n', s3, '\\n')\n",
        "\n",
        "# Accessing elements of a Series\n",
        "\n",
        "print('s3[2]=', s3[2])        # display third element of the Series\n",
        "print('s3[\\'Jan 3\\']=', s3['Jan 3'])   # indexing element of a Series\n",
        "\n",
        "print('\\ns3[1:3]=')             # display a slice of the Series\n",
        "print(s3[1:3])\n",
        "print('\\ns3.iloc([1:3])=')      # display a slice of the Series\n",
        "print(s3.iloc[1:3])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xcaz3h6D8eFY"
      },
      "source": [
        "There are various functions available to find the number of elements in a Series. Result of the function depends on whether null elements are included."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "sfLT6w5Q8eFY"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Series s3 =\n",
            " Jan 1    1.2\n",
            "Jan 2    2.5\n",
            "Jan 3   -2.2\n",
            "Jan 4    3.1\n",
            "Jan 5   -0.8\n",
            "Jan 6   -3.2\n",
            "Jan 7    NaN\n",
            "dtype: float64 \n",
            "\n",
            "Shape of s3 = (7,)\n",
            "Size of s3 = 7\n",
            "Count of s3 = 6\n"
          ]
        }
      ],
      "source": [
        "s3['Jan 7'] = np.nan\n",
        "print('Series s3 =\\n', s3, '\\n')\n",
        "\n",
        "print('Shape of s3 =', s3.shape)   # get the dimension of the Series\n",
        "print('Size of s3 =', s3.size)     # get the number of elements of the Series\n",
        "print('Count of s3 =', s3.count()) # get the number of non-null elements of the Series"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wn3HIvUw8eFY"
      },
      "source": [
        "A boolean filter can be used to select elements of a Series"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "JTq2jNlb8eFY"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Jan 1    1.2\n",
            "Jan 2    2.5\n",
            "Jan 4    3.1\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(s3[s3 > 0])   # applying filter to select non-negative elements of the Series"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "firearHG8eFY"
      },
      "source": [
        "Scalar operations can be performed on elements of a numeric Series"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "uqsyZpGk8eFY"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "s3 + 4 =\n",
            " Jan 1    5.2\n",
            "Jan 2    6.5\n",
            "Jan 3    1.8\n",
            "Jan 4    7.1\n",
            "Jan 5    3.2\n",
            "Jan 6    0.8\n",
            "Jan 7    NaN\n",
            "dtype: float64 \n",
            "\n",
            "s3 / 4 =\n",
            " Jan 1    0.300\n",
            "Jan 2    0.625\n",
            "Jan 3   -0.550\n",
            "Jan 4    0.775\n",
            "Jan 5   -0.200\n",
            "Jan 6   -0.800\n",
            "Jan 7      NaN\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print('s3 + 4 =\\n', s3 + 4, '\\n')\n",
        "print('s3 / 4 =\\n', s3 / 4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5q3wHOuQ8eFY"
      },
      "source": [
        "Numpy functions can be applied to pandas Series."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "T2EfiGs38eFZ"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "np.log(s3 + 4) =\n",
            " Jan 1    1.648659\n",
            "Jan 2    1.871802\n",
            "Jan 3    0.587787\n",
            "Jan 4    1.960095\n",
            "Jan 5    1.163151\n",
            "Jan 6   -0.223144\n",
            "Jan 7         NaN\n",
            "dtype: float64 \n",
            "\n",
            "np.exp(s3 - 4) =\n",
            " Jan 1    0.060810\n",
            "Jan 2    0.223130\n",
            "Jan 3    0.002029\n",
            "Jan 4    0.406570\n",
            "Jan 5    0.008230\n",
            "Jan 6    0.000747\n",
            "Jan 7         NaN\n",
            "dtype: float64 \n",
            "\n"
          ]
        }
      ],
      "source": [
        "print('np.log(s3 + 4) =\\n', np.log(s3 + 4), '\\n')    # applying log function to a numeric Series\n",
        "print('np.exp(s3 - 4) =\\n', np.exp(s3 - 4), '\\n')    # applying exponent function to a numeric Series"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "onCboFfz8eFZ"
      },
      "source": [
        "The value_counts() function can be used for tabulating the counts of each discrete value in the Series."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "id": "7gI9kVTS8eFZ"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "colors =\n",
            " 0       red\n",
            "1      blue\n",
            "2      blue\n",
            "3    yellow\n",
            "4       red\n",
            "5     green\n",
            "6      blue\n",
            "7       NaN\n",
            "dtype: object \n",
            "\n",
            "colors.value_counts() =\n",
            " blue      3\n",
            "red       2\n",
            "yellow    1\n",
            "green     1\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "colors = Series(['red', 'blue', 'blue', 'yellow', 'red', 'green', 'blue', np.nan])\n",
        "print('colors =\\n', colors, '\\n')\n",
        "\n",
        "print('colors.value_counts() =\\n', colors.value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IlJI16vL8eFZ"
      },
      "source": [
        "### 2.2.2 DataFrame\n",
        "\n",
        "A DataFrame object is a tabular, spreadsheet-like data structure containing a collection of columns, each of which can be of different types (numeric, string, boolean, etc). Unlike Series, a DataFrame has distinct row and column indices. There are many ways to create a DataFrame object (e.g., from a dictionary, list of tuples, or even numpy's ndarrays)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "fPdfn_sV8eFZ"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>make</th>\n",
              "      <th>model</th>\n",
              "      <th>MSRP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ford</td>\n",
              "      <td>Taurus</td>\n",
              "      <td>27595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Honda</td>\n",
              "      <td>Accord</td>\n",
              "      <td>23570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Toyota</td>\n",
              "      <td>Camry</td>\n",
              "      <td>23495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Tesla</td>\n",
              "      <td>Model S</td>\n",
              "      <td>68000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     make    model   MSRP\n",
              "0    Ford   Taurus  27595\n",
              "1   Honda   Accord  23570\n",
              "2  Toyota    Camry  23495\n",
              "3   Tesla  Model S  68000"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from pandas import DataFrame\n",
        "\n",
        "cars = {'make': ['Ford', 'Honda', 'Toyota', 'Tesla'],\n",
        "       'model': ['Taurus', 'Accord', 'Camry', 'Model S'],\n",
        "       'MSRP': [27595, 23570, 23495, 68000]}\n",
        "carData = DataFrame(cars)            # creating DataFrame from dictionary\n",
        "carData                              # display the table"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "7MHaOU4h8eFZ"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "carData.index = RangeIndex(start=0, stop=4, step=1)\n",
            "carData.columns = Index(['make', 'model', 'MSRP'], dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print('carData.index =', carData.index)         # print the row indices\n",
        "print('carData.columns =', carData.columns)     # print the column indices"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pQu6pJ1t8eFZ"
      },
      "source": [
        "Inserting columns to an existing dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "id": "JrNwrxOG8eFa"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>make</th>\n",
              "      <th>model</th>\n",
              "      <th>MSRP</th>\n",
              "      <th>year</th>\n",
              "      <th>dealership</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ford</td>\n",
              "      <td>Taurus</td>\n",
              "      <td>27595</td>\n",
              "      <td>2018</td>\n",
              "      <td>Courtesy Ford</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Honda</td>\n",
              "      <td>Accord</td>\n",
              "      <td>23570</td>\n",
              "      <td>2018</td>\n",
              "      <td>Capital Honda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Toyota</td>\n",
              "      <td>Camry</td>\n",
              "      <td>23495</td>\n",
              "      <td>2018</td>\n",
              "      <td>Spartan Toyota</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Tesla</td>\n",
              "      <td>Model S</td>\n",
              "      <td>68000</td>\n",
              "      <td>2018</td>\n",
              "      <td>N/A</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     make    model   MSRP  year      dealership\n",
              "1    Ford   Taurus  27595  2018   Courtesy Ford\n",
              "2   Honda   Accord  23570  2018   Capital Honda\n",
              "3  Toyota    Camry  23495  2018  Spartan Toyota\n",
              "4   Tesla  Model S  68000  2018             N/A"
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "carData2 = DataFrame(cars, index = [1,2,3,4])  # change the row index\n",
        "carData2['year'] = 2018    # add column with same value\n",
        "carData2['dealership'] = ['Courtesy Ford','Capital Honda','Spartan Toyota','N/A']\n",
        "carData2                   # display table"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fHHYnaOV8eFa"
      },
      "source": [
        "Creating DataFrame from a list of tuples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "WTQbXh__8eFa"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>temp</th>\n",
              "      <th>precip</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011</td>\n",
              "      <td>45.1</td>\n",
              "      <td>32.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012</td>\n",
              "      <td>42.4</td>\n",
              "      <td>34.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013</td>\n",
              "      <td>47.2</td>\n",
              "      <td>39.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014</td>\n",
              "      <td>44.2</td>\n",
              "      <td>31.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015</td>\n",
              "      <td>39.9</td>\n",
              "      <td>29.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2016</td>\n",
              "      <td>41.5</td>\n",
              "      <td>36.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   year  temp  precip\n",
              "0  2011  45.1    32.4\n",
              "1  2012  42.4    34.5\n",
              "2  2013  47.2    39.2\n",
              "3  2014  44.2    31.4\n",
              "4  2015  39.9    29.8\n",
              "5  2016  41.5    36.7"
            ]
          },
          "execution_count": 89,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tuplelist = [(2011,45.1,32.4),(2012,42.4,34.5),(2013,47.2,39.2),\n",
        "              (2014,44.2,31.4),(2015,39.9,29.8),(2016,41.5,36.7)]\n",
        "columnNames = ['year','temp','precip']\n",
        "weatherData = DataFrame(tuplelist, columns=columnNames)\n",
        "weatherData"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bofDdBCV8eFa"
      },
      "source": [
        "Creating DataFrame from numpy ndarray"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "id": "knqwXGk_8eFa"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x1</th>\n",
              "      <th>x2</th>\n",
              "      <th>x3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.309711</td>\n",
              "      <td>0.570444</td>\n",
              "      <td>-3.103342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.860892</td>\n",
              "      <td>-0.905170</td>\n",
              "      <td>-0.548246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.757596</td>\n",
              "      <td>1.116583</td>\n",
              "      <td>1.434651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.212755</td>\n",
              "      <td>-0.183329</td>\n",
              "      <td>-0.360286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.359486</td>\n",
              "      <td>-0.743659</td>\n",
              "      <td>1.108049</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         x1        x2        x3\n",
              "0  1.309711  0.570444 -3.103342\n",
              "1 -0.860892 -0.905170 -0.548246\n",
              "2  0.757596  1.116583  1.434651\n",
              "3 -0.212755 -0.183329 -0.360286\n",
              "4 -1.359486 -0.743659  1.108049"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "npdata = np.random.randn(5,3)  # create a 5 by 3 random matrix\n",
        "columnNames = ['x1','x2','x3']\n",
        "data = DataFrame(npdata, columns=columnNames)\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "McY-K1-U8eFa"
      },
      "source": [
        "There are many ways to access elements of a DataFrame object."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "YTA5pUuu8eFa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    0.570444\n",
            "1   -0.905170\n",
            "2    1.116583\n",
            "3   -0.183329\n",
            "4   -0.743659\n",
            "Name: x2, dtype: float64\n",
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ],
      "source": [
        "# accessing an entire column will return a Series object\n",
        "\n",
        "print(data['x2'])\n",
        "print(type(data['x2']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "t1S0cCCh8eFa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Row 3 of data table:\n",
            "x1    0.757596\n",
            "x2    1.116583\n",
            "x3    1.434651\n",
            "Name: 2, dtype: float64\n",
            "<class 'pandas.core.series.Series'>\n",
            "\n",
            "Row 3 of car data table:\n",
            "make                  Toyota\n",
            "model                  Camry\n",
            "MSRP                   23495\n",
            "year                    2018\n",
            "dealership    Spartan Toyota\n",
            "Name: 3, dtype: object\n"
          ]
        }
      ],
      "source": [
        "# accessing an entire row will return a Series object\n",
        "\n",
        "print('Row 3 of data table:')\n",
        "print(data.iloc[2])       # returns the 3rd row of DataFrame\n",
        "print(type(data.iloc[2]))\n",
        "\n",
        "print('\\nRow 3 of car data table:')\n",
        "print(carData2.iloc[2])   # row contains objects of different types"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "h4M9KlcQ8eFb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "carData2 =\n",
            "      make    model   MSRP  year      dealership\n",
            "1    Ford   Taurus  27595  2018   Courtesy Ford\n",
            "2   Honda   Accord  23570  2018   Capital Honda\n",
            "3  Toyota    Camry  23495  2018  Spartan Toyota\n",
            "4   Tesla  Model S  68000  2018             N/A\n",
            "\n",
            "carData2.iloc[1,2] = 23570\n",
            "carData2.loc[1,'model'] = Taurus\n",
            "\n",
            "carData2.iloc[1:3,1:3]=\n",
            "    model   MSRP\n",
            "2  Accord  23570\n",
            "3   Camry  23495\n"
          ]
        }
      ],
      "source": [
        "# accessing a specific element of the DataFrame\n",
        "\n",
        "print('carData2 =\\n', carData2)\n",
        "\n",
        "print('\\ncarData2.iloc[1,2] =', carData2.iloc[1,2])                # retrieving second row, third column\n",
        "print('carData2.loc[1,\\'model\\'] =', carData2.loc[1,'model'])    # retrieving second row, column named 'model'\n",
        "\n",
        "# accessing a slice of the DataFrame\n",
        "\n",
        "print('\\ncarData2.iloc[1:3,1:3]=')\n",
        "print(carData2.iloc[1:3,1:3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "t2IkCL6m8eFb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "carData2 =\n",
            "      make    model   MSRP  year      dealership\n",
            "1    Ford   Taurus  27595  2018   Courtesy Ford\n",
            "2   Honda   Accord  23570  2018   Capital Honda\n",
            "3  Toyota    Camry  23495  2018  Spartan Toyota\n",
            "4   Tesla  Model S  68000  2018             N/A \n",
            "\n",
            "carData2.shape = (4, 5)\n",
            "carData2.size = 20\n"
          ]
        }
      ],
      "source": [
        "print('carData2 =\\n', carData2, '\\n')\n",
        "\n",
        "print('carData2.shape =', carData2.shape)\n",
        "print('carData2.size =', carData2.size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "9FsF6Aq88eFb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "carData2 =\n",
            "      make    model   MSRP  year      dealership\n",
            "1    Ford   Taurus  27595  2018   Courtesy Ford\n",
            "2   Honda   Accord  23570  2018   Capital Honda\n",
            "3  Toyota    Camry  23495  2018  Spartan Toyota\n",
            "4   Tesla  Model S  68000  2018             N/A \n",
            "\n",
            "carData2[carData2.MSRP > 25000] =\n",
            "    make    model   MSRP  year     dealership\n",
            "1   Ford   Taurus  27595  2018  Courtesy Ford\n",
            "4  Tesla  Model S  68000  2018            N/A\n"
          ]
        }
      ],
      "source": [
        "# selection and filtering\n",
        "\n",
        "print('carData2 =\\n', carData2, '\\n')\n",
        "\n",
        "print('carData2[carData2.MSRP > 25000] =')\n",
        "print(carData2[carData2.MSRP > 25000])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_dJKputS8eFb"
      },
      "source": [
        "### 2.2.3 Arithmetic Operations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "lYrSECdU8eFb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "         x1        x2        x3\n",
            "0  1.309711  0.570444 -3.103342\n",
            "1 -0.860892 -0.905170 -0.548246\n",
            "2  0.757596  1.116583  1.434651\n",
            "3 -0.212755 -0.183329 -0.360286\n",
            "4 -1.359486 -0.743659  1.108049\n",
            "\n",
            "Data transpose operation: data.T\n",
            "           0         1         2         3         4\n",
            "x1  1.309711 -0.860892  0.757596 -0.212755 -1.359486\n",
            "x2  0.570444 -0.905170  1.116583 -0.183329 -0.743659\n",
            "x3 -3.103342 -0.548246  1.434651 -0.360286  1.108049\n",
            "\n",
            "Addition: data + 4\n",
            "         x1        x2        x3\n",
            "0  5.309711  4.570444  0.896658\n",
            "1  3.139108  3.094830  3.451754\n",
            "2  4.757596  5.116583  5.434651\n",
            "3  3.787245  3.816671  3.639714\n",
            "4  2.640514  3.256341  5.108049\n",
            "\n",
            "Multiplication: data * 10\n",
            "          x1         x2         x3\n",
            "0  13.097109   5.704438 -31.033419\n",
            "1  -8.608923  -9.051701  -5.482464\n",
            "2   7.575962  11.165829  14.346507\n",
            "3  -2.127554  -1.833289  -3.602859\n",
            "4 -13.594860  -7.436587  11.080488\n"
          ]
        }
      ],
      "source": [
        "print(data)\n",
        "\n",
        "print('\\nData transpose operation: data.T')\n",
        "print(data.T)    # transpose operation\n",
        "\n",
        "print('\\nAddition: data + 4')\n",
        "print(data + 4)    # addition operation\n",
        "\n",
        "print('\\nMultiplication: data * 10')\n",
        "print(data * 10)   # multiplication operation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "id": "5tBpw4z28eFb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "data =\n",
            "          x1        x2        x3\n",
            "0  1.309711  0.570444 -3.103342\n",
            "1 -0.860892 -0.905170 -0.548246\n",
            "2  0.757596  1.116583  1.434651\n",
            "3 -0.212755 -0.183329 -0.360286\n",
            "4 -1.359486 -0.743659  1.108049\n",
            "\n",
            "data2 =\n",
            "         x1        x2        x3\n",
            "0 -3.050164 -0.394077  1.118160\n",
            "1 -1.253780  0.780094  0.367442\n",
            "2 -0.129573 -1.870528 -1.028147\n",
            "3 -0.402716  1.192577  0.524447\n",
            "4 -0.530671  0.073868 -0.366333\n",
            "\n",
            "data + data2 = \n",
            "         x1        x2        x3\n",
            "0 -1.740453  0.176367 -1.985182\n",
            "1 -2.114672 -0.125076 -0.180804\n",
            "2  0.628023 -0.753945  0.406504\n",
            "3 -0.615472  1.009248  0.164161\n",
            "4 -1.890157 -0.669791  0.741716\n",
            "\n",
            "data * data2 = \n",
            "         x1        x2        x3\n",
            "0 -3.994834 -0.224799 -3.470032\n",
            "1  1.079370 -0.706118 -0.201449\n",
            "2 -0.098164 -2.088599 -1.475031\n",
            "3  0.085680 -0.218634 -0.188951\n",
            "4  0.721440 -0.054932 -0.405915\n"
          ]
        }
      ],
      "source": [
        "print('data =\\n', data)\n",
        "\n",
        "columnNames = ['x1','x2','x3']\n",
        "data2 = DataFrame(np.random.randn(5,3), columns=columnNames)\n",
        "print('\\ndata2 =')\n",
        "print(data2)\n",
        "\n",
        "print('\\ndata + data2 = ')\n",
        "print(data.add(data2))\n",
        "\n",
        "print('\\ndata * data2 = ')\n",
        "print(data.mul(data2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "qJIpfrea8eFb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "         x1        x2        x3\n",
            "0  1.309711  0.570444  3.103342\n",
            "1  0.860892  0.905170  0.548246\n",
            "2  0.757596  1.116583  1.434651\n",
            "3  0.212755  0.183329  0.360286\n",
            "4  1.359486  0.743659  1.108049\n",
            "\n",
            "Maximum value per column:\n",
            "x1    1.309711\n",
            "x2    1.116583\n",
            "x3    1.434651\n",
            "dtype: float64\n",
            "\n",
            "Minimum value per row:\n",
            "0   -3.103342\n",
            "1   -0.905170\n",
            "2    0.757596\n",
            "3   -0.360286\n",
            "4   -1.359486\n",
            "dtype: float64\n",
            "\n",
            "Sum of values per column:\n",
            "x1   -0.365827\n",
            "x2   -0.145131\n",
            "x3   -1.469175\n",
            "dtype: float64\n",
            "\n",
            "Average value per row:\n",
            "0   -0.407729\n",
            "1   -0.771436\n",
            "2    1.102943\n",
            "3   -0.252123\n",
            "4   -0.331699\n",
            "dtype: float64\n",
            "\n",
            "Calculate max - min per column\n",
            "x1    2.669197\n",
            "x2    2.021753\n",
            "x3    4.537993\n",
            "dtype: float64\n",
            "\n",
            "Calculate max - min per row\n",
            "0    4.413053\n",
            "1    0.356924\n",
            "2    0.677055\n",
            "3    0.176957\n",
            "4    2.467535\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(data.abs())    # get the absolute value for each element\n",
        "\n",
        "print('\\nMaximum value per column:')\n",
        "print(data.max())    # get maximum value for each column\n",
        "\n",
        "print('\\nMinimum value per row:')\n",
        "print(data.min(axis=1))    # get minimum value for each row\n",
        "\n",
        "print('\\nSum of values per column:')\n",
        "print(data.sum())    # get sum of values for each column\n",
        "\n",
        "print('\\nAverage value per row:')\n",
        "print(data.mean(axis=1))    # get average value for each row\n",
        "\n",
        "print('\\nCalculate max - min per column')\n",
        "f = lambda x: x.max() - x.min()\n",
        "print(data.apply(f))\n",
        "\n",
        "print('\\nCalculate max - min per row')\n",
        "f = lambda x: x.max() - x.min()\n",
        "print(data.apply(f, axis=1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Dvsy6-Z8eFc"
      },
      "source": [
        "The value_counts() function can also be applied to a pandas DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "5BlFqYWK8eFc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "shapeData =\n",
            "        shape color\n",
            "0     circle   red\n",
            "1     square   red\n",
            "2     square   red\n",
            "3     square  blue\n",
            "4     circle  blue\n",
            "5  rectangle  blue \n",
            "\n",
            "shapeData.value_counts() =\n",
            " shape      color\n",
            "circle     blue     1\n",
            "           red      1\n",
            "rectangle  blue     1\n",
            "square     blue     1\n",
            "           red      2\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "objects = {'shape': ['circle', 'square', 'square', 'square', 'circle', 'rectangle'],\n",
        "           'color': ['red', 'red', 'red', 'blue', 'blue', 'blue']}\n",
        "\n",
        "shapeData = DataFrame(objects)\n",
        "print('shapeData =\\n', shapeData, '\\n')\n",
        "\n",
        "print('shapeData.value_counts() =\\n', shapeData.value_counts().sort_values())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-6KfhnE8eFc"
      },
      "source": [
        "### 2.2.4 Plotting Series and DataFrame\n",
        "\n",
        "There are many built-in functions available to plot the data stored in a Series or a DataFrame.\n",
        "\n",
        "**(a)** Line plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "id": "SJBaRX5o_pEf"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "\n",
        "s3 = Series([1.2,2.5,-2.2,3.1,-0.8,-3.2,1.4],\n",
        "            index = ['Jan 1','Jan 2','Jan 3','Jan 4','Jan 5','Jan 6','Jan 7'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "BNgGHmhm8eFc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Line plot'}>"
            ]
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "s3.plot(kind='line', title='Line plot')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mmuEOof8eFc"
      },
      "source": [
        "**(b)** Bar plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "id": "cdUTeqWZ8eFc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Bar plot'}>"
            ]
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "s3.plot(kind='bar', title='Bar plot')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JUJqhXlA8eFc"
      },
      "source": [
        "**(c)** Histogram"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "swlPhNvm8eFc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Histogram'}, ylabel='Frequency'>"
            ]
          },
          "execution_count": 104,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "s3.plot(kind='hist', title = 'Histogram')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0L5UZaeR8eFc"
      },
      "source": [
        "**(d)** Box plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "id": "Ox2Ftgh-8eFd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Box plot'}>"
            ]
          },
          "execution_count": 105,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tuplelist = [(2011,45.1,32.4),(2012,42.4,34.5),(2013,47.2,39.2),\n",
        "              (2014,44.2,31.4),(2015,39.9,29.8),(2016,41.5,36.7)]\n",
        "columnNames = ['year','temp','precip']\n",
        "weatherData = DataFrame(tuplelist, columns=columnNames)\n",
        "weatherData[['temp','precip']].plot(kind='box', title='Box plot')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lHrSTDEY8eFd"
      },
      "source": [
        "**(e)** Scatter plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "id": "nxzsPmi98eFd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "weatherData =\n",
            "    year  temp  precip\n",
            "0  2011  45.1    32.4\n",
            "1  2012  42.4    34.5\n",
            "2  2013  47.2    39.2\n",
            "3  2014  44.2    31.4\n",
            "4  2015  39.9    29.8\n",
            "5  2016  41.5    36.7\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='temp', ylabel='precip'>"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "print('weatherData =\\n', weatherData)\n",
        "\n",
        "weatherData.plot(kind='scatter', x='temp', y='precip')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nseafkAvB3SH"
      },
      "source": [
        "# Exercise: Explore crop production dataset\n",
        "\n",
        "The dataset `Production_Crops_E_Asia.csv` contains crop production statistics from the United Nations Food and Agriculture Organization (UN FAO) in East Asian countries from 1961 to 2019 (source: https://data.world/agriculture/crop-production). In this exercise, you'll explore the dataset using some of the operations from Modules 1-2 in this lab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "2378EspcCT4d"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "qSirJQ-E_wvk"
      },
      "outputs": [],
      "source": [
        "# Load the CSV file from its storage location on Google Drive\n",
        "prod = pd.read_csv('https://docs.google.com/uc?export=download&id=1r2DzEFS5tb5NjoIyAl6FvUDYW5DOKm5q')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "G2zrna493Xgc",
        "outputId": "5e630f34-f007-458c-c494-ea730bc82332"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area Code</th>\n",
              "      <th>Area</th>\n",
              "      <th>Item Code</th>\n",
              "      <th>Item</th>\n",
              "      <th>Element Code</th>\n",
              "      <th>Element</th>\n",
              "      <th>Unit</th>\n",
              "      <th>Y1961</th>\n",
              "      <th>Y1961F</th>\n",
              "      <th>Y1962</th>\n",
              "      <th>...</th>\n",
              "      <th>Y2015</th>\n",
              "      <th>Y2015F</th>\n",
              "      <th>Y2016</th>\n",
              "      <th>Y2016F</th>\n",
              "      <th>Y2017</th>\n",
              "      <th>Y2017F</th>\n",
              "      <th>Y2018</th>\n",
              "      <th>Y2018F</th>\n",
              "      <th>Y2019</th>\n",
              "      <th>Y2019F</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>221</td>\n",
              "      <td>Almonds, with shell</td>\n",
              "      <td>5312</td>\n",
              "      <td>Area harvested</td>\n",
              "      <td>ha</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>14676.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19481.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19793.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20053.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29203.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>221</td>\n",
              "      <td>Almonds, with shell</td>\n",
              "      <td>5419</td>\n",
              "      <td>Yield</td>\n",
              "      <td>hg/ha</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>16521.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>16859.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>13788.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>17161.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>13083.0</td>\n",
              "      <td>Fc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>221</td>\n",
              "      <td>Almonds, with shell</td>\n",
              "      <td>5510</td>\n",
              "      <td>Production</td>\n",
              "      <td>tonnes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>24246.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32843.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27291.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34413.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38205.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>711</td>\n",
              "      <td>Anise, badian, fennel, coriander</td>\n",
              "      <td>5312</td>\n",
              "      <td>Area harvested</td>\n",
              "      <td>ha</td>\n",
              "      <td>NaN</td>\n",
              "      <td>M</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>25000.0</td>\n",
              "      <td>F</td>\n",
              "      <td>25787.0</td>\n",
              "      <td>Im</td>\n",
              "      <td>28398.0</td>\n",
              "      <td>Im</td>\n",
              "      <td>26725.0</td>\n",
              "      <td>Im</td>\n",
              "      <td>27562.0</td>\n",
              "      <td>Im</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>711</td>\n",
              "      <td>Anise, badian, fennel, coriander</td>\n",
              "      <td>5419</td>\n",
              "      <td>Yield</td>\n",
              "      <td>hg/ha</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>7200.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>6982.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>6863.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>6898.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>6903.0</td>\n",
              "      <td>Fc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9633</th>\n",
              "      <td>249</td>\n",
              "      <td>Yemen</td>\n",
              "      <td>1729</td>\n",
              "      <td>Treenuts, Total</td>\n",
              "      <td>5419</td>\n",
              "      <td>Yield</td>\n",
              "      <td>hg/ha</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>18805.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>18061.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>17863.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>17513.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>17516.0</td>\n",
              "      <td>Fc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9634</th>\n",
              "      <td>249</td>\n",
              "      <td>Yemen</td>\n",
              "      <td>1729</td>\n",
              "      <td>Treenuts, Total</td>\n",
              "      <td>5510</td>\n",
              "      <td>Production</td>\n",
              "      <td>tonnes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>11014.0</td>\n",
              "      <td>A</td>\n",
              "      <td>10591.0</td>\n",
              "      <td>A</td>\n",
              "      <td>10484.0</td>\n",
              "      <td>A</td>\n",
              "      <td>10483.0</td>\n",
              "      <td>A</td>\n",
              "      <td>11340.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9635</th>\n",
              "      <td>249</td>\n",
              "      <td>Yemen</td>\n",
              "      <td>1735</td>\n",
              "      <td>Vegetables Primary</td>\n",
              "      <td>5312</td>\n",
              "      <td>Area harvested</td>\n",
              "      <td>ha</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>A</td>\n",
              "      <td>7100.0</td>\n",
              "      <td>...</td>\n",
              "      <td>42489.0</td>\n",
              "      <td>A</td>\n",
              "      <td>39887.0</td>\n",
              "      <td>A</td>\n",
              "      <td>38612.0</td>\n",
              "      <td>A</td>\n",
              "      <td>40647.0</td>\n",
              "      <td>A</td>\n",
              "      <td>41528.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9636</th>\n",
              "      <td>249</td>\n",
              "      <td>Yemen</td>\n",
              "      <td>1735</td>\n",
              "      <td>Vegetables Primary</td>\n",
              "      <td>5419</td>\n",
              "      <td>Yield</td>\n",
              "      <td>hg/ha</td>\n",
              "      <td>107143.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>107042.0</td>\n",
              "      <td>...</td>\n",
              "      <td>113518.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>106887.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>107777.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>112627.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>115155.0</td>\n",
              "      <td>Fc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9637</th>\n",
              "      <td>249</td>\n",
              "      <td>Yemen</td>\n",
              "      <td>1735</td>\n",
              "      <td>Vegetables Primary</td>\n",
              "      <td>5510</td>\n",
              "      <td>Production</td>\n",
              "      <td>tonnes</td>\n",
              "      <td>75000.0</td>\n",
              "      <td>A</td>\n",
              "      <td>76000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>482325.0</td>\n",
              "      <td>A</td>\n",
              "      <td>426339.0</td>\n",
              "      <td>A</td>\n",
              "      <td>416147.0</td>\n",
              "      <td>A</td>\n",
              "      <td>457796.0</td>\n",
              "      <td>A</td>\n",
              "      <td>478214.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9638 rows × 125 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Area Code         Area  Item Code                              Item  \\\n",
              "0             2  Afghanistan        221               Almonds, with shell   \n",
              "1             2  Afghanistan        221               Almonds, with shell   \n",
              "2             2  Afghanistan        221               Almonds, with shell   \n",
              "3             2  Afghanistan        711  Anise, badian, fennel, coriander   \n",
              "4             2  Afghanistan        711  Anise, badian, fennel, coriander   \n",
              "...         ...          ...        ...                               ...   \n",
              "9633        249        Yemen       1729                   Treenuts, Total   \n",
              "9634        249        Yemen       1729                   Treenuts, Total   \n",
              "9635        249        Yemen       1735                Vegetables Primary   \n",
              "9636        249        Yemen       1735                Vegetables Primary   \n",
              "9637        249        Yemen       1735                Vegetables Primary   \n",
              "\n",
              "      Element Code         Element    Unit     Y1961 Y1961F     Y1962  ...  \\\n",
              "0             5312  Area harvested      ha       NaN    NaN       NaN  ...   \n",
              "1             5419           Yield   hg/ha       NaN    NaN       NaN  ...   \n",
              "2             5510      Production  tonnes       NaN    NaN       NaN  ...   \n",
              "3             5312  Area harvested      ha       NaN      M       NaN  ...   \n",
              "4             5419           Yield   hg/ha       NaN    NaN       NaN  ...   \n",
              "...            ...             ...     ...       ...    ...       ...  ...   \n",
              "9633          5419           Yield   hg/ha       NaN    NaN       NaN  ...   \n",
              "9634          5510      Production  tonnes       NaN    NaN       NaN  ...   \n",
              "9635          5312  Area harvested      ha    7000.0      A    7100.0  ...   \n",
              "9636          5419           Yield   hg/ha  107143.0     Fc  107042.0  ...   \n",
              "9637          5510      Production  tonnes   75000.0      A   76000.0  ...   \n",
              "\n",
              "         Y2015  Y2015F     Y2016  Y2016F     Y2017  Y2017F     Y2018  Y2018F  \\\n",
              "0      14676.0     NaN   19481.0     NaN   19793.0     NaN   20053.0     NaN   \n",
              "1      16521.0      Fc   16859.0      Fc   13788.0      Fc   17161.0      Fc   \n",
              "2      24246.0     NaN   32843.0     NaN   27291.0     NaN   34413.0     NaN   \n",
              "3      25000.0       F   25787.0      Im   28398.0      Im   26725.0      Im   \n",
              "4       7200.0      Fc    6982.0      Fc    6863.0      Fc    6898.0      Fc   \n",
              "...        ...     ...       ...     ...       ...     ...       ...     ...   \n",
              "9633   18805.0      Fc   18061.0      Fc   17863.0      Fc   17513.0      Fc   \n",
              "9634   11014.0       A   10591.0       A   10484.0       A   10483.0       A   \n",
              "9635   42489.0       A   39887.0       A   38612.0       A   40647.0       A   \n",
              "9636  113518.0      Fc  106887.0      Fc  107777.0      Fc  112627.0      Fc   \n",
              "9637  482325.0       A  426339.0       A  416147.0       A  457796.0       A   \n",
              "\n",
              "         Y2019  Y2019F  \n",
              "0      29203.0     NaN  \n",
              "1      13083.0      Fc  \n",
              "2      38205.0     NaN  \n",
              "3      27562.0      Im  \n",
              "4       6903.0      Fc  \n",
              "...        ...     ...  \n",
              "9633   17516.0      Fc  \n",
              "9634   11340.0       A  \n",
              "9635   41528.0       A  \n",
              "9636  115155.0      Fc  \n",
              "9637  478214.0       A  \n",
              "\n",
              "[9638 rows x 125 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prod"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iU5MsVJu3Xgc"
      },
      "source": [
        "How many unique countries are in the dataset? What are the names of the countries? Print a list of the unique countries from the 'Area' column as well as the number of unique countries."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "2lNR3pg63Xgc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Afghanistan' 'Armenia' 'Azerbaijan' 'Bahrain' 'Bangladesh' 'Bhutan'\n",
            " 'Brunei Darussalam' 'Cambodia' 'China, Hong Kong SAR' 'China, Macao SAR'\n",
            " 'China, mainland' 'China, Taiwan Province of' 'Cyprus'\n",
            " \"Democratic People's Republic of Korea\" 'Georgia' 'India' 'Indonesia'\n",
            " 'Iran (Islamic Republic of)' 'Iraq' 'Israel' 'Japan' 'Jordan'\n",
            " 'Kazakhstan' 'Kuwait' 'Kyrgyzstan' \"Lao People's Democratic Republic\"\n",
            " 'Lebanon' 'Malaysia' 'Maldives' 'Mongolia' 'Myanmar' 'Nepal' 'Oman'\n",
            " 'Pakistan' 'Palestine' 'Philippines' 'Qatar' 'Republic of Korea'\n",
            " 'Saudi Arabia' 'Singapore' 'Sri Lanka' 'Syrian Arab Republic'\n",
            " 'Tajikistan' 'Thailand' 'Timor-Leste' 'Turkey' 'Turkmenistan'\n",
            " 'United Arab Emirates' 'Uzbekistan' 'Viet Nam' 'Yemen']\n",
            "Number of unique countries: 51\n"
          ]
        }
      ],
      "source": [
        "print(prod['Area'].unique())\n",
        "print('Number of unique countries: {}'.format(len(prod['Area'].unique())))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7C6rzZOH3Xgc"
      },
      "source": [
        "What are the most common crops included in the dataset across all countries? Use value_counts() to print the number of occurrences of each crop category (column 'Item') in the dataframe."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "k1BA3EZu3Xgc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Item\n",
              "Roots and Tubers, Total    152\n",
              "Vegetables, fresh nes      152\n",
              "Vegetables Primary         152\n",
              "Fruit Primary              150\n",
              "Cereals, Total             144\n",
              "                          ... \n",
              "Tallowtree seed              3\n",
              "Gooseberries                 3\n",
              "Agave fibres nes             3\n",
              "Karite nuts (sheanuts)       2\n",
              "Cassava leaves               2\n",
              "Name: count, Length: 165, dtype: int64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prod['Item'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UtO5cdTq3Xgc"
      },
      "source": [
        "Print a list of the countries in the dataframe sorted by the number of rows in which it appears."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "S_nGbh_K3Xgd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('China, Macao SAR', 23),\n",
              " ('China, Hong Kong SAR', 59),\n",
              " ('Mongolia', 74),\n",
              " ('Singapore', 76),\n",
              " ('Bahrain', 89),\n",
              " ('Maldives', 93),\n",
              " ('Qatar', 101),\n",
              " ('Oman', 102),\n",
              " ('Brunei Darussalam', 105),\n",
              " ('Saudi Arabia', 112),\n",
              " ('United Arab Emirates', 112),\n",
              " ('Kuwait', 119),\n",
              " ('Timor-Leste', 119),\n",
              " ('Turkmenistan', 119),\n",
              " ('Cambodia', 128),\n",
              " (\"Democratic People's Republic of Korea\", 138),\n",
              " (\"Lao People's Democratic Republic\", 138),\n",
              " ('Afghanistan', 145),\n",
              " ('Armenia', 164),\n",
              " ('Viet Nam', 165),\n",
              " ('Myanmar', 168),\n",
              " ('Tajikistan', 172),\n",
              " ('Malaysia', 174),\n",
              " ('Bhutan', 178),\n",
              " ('Sri Lanka', 180),\n",
              " ('Georgia', 190),\n",
              " ('Nepal', 199),\n",
              " ('Yemen', 208),\n",
              " ('Iraq', 220),\n",
              " ('Indonesia', 221),\n",
              " ('Bangladesh', 223),\n",
              " ('Jordan', 224),\n",
              " ('Lebanon', 228),\n",
              " ('Azerbaijan', 230),\n",
              " ('Kazakhstan', 230),\n",
              " ('Republic of Korea', 230),\n",
              " ('Palestine', 243),\n",
              " ('Kyrgyzstan', 251),\n",
              " ('Philippines', 254),\n",
              " ('Syrian Arab Republic', 254),\n",
              " ('Thailand', 257),\n",
              " ('Israel', 259),\n",
              " ('Pakistan', 263),\n",
              " ('Uzbekistan', 264),\n",
              " ('China, Taiwan Province of', 267),\n",
              " ('Cyprus', 273),\n",
              " ('India', 284),\n",
              " ('Japan', 288),\n",
              " ('Iran (Islamic Republic of)', 302),\n",
              " ('Turkey', 325),\n",
              " ('China, mainland', 398)]"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sorted([(country, df.shape[0]) for country, df in prod.groupby('Area')], key=lambda x: x[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IOQD2loo3Xgd"
      },
      "source": [
        "What are the trends in crop production for cereal crops (includes maize, wheat, etc.) from 1961 to 2019? Plot a line plot of crop production for the following countries: India, Japan, Iran, Turkey, and China (mainland). Only plot the values in the columns with format 'YXXXX'."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6BCB4dv3Xgd"
      },
      "source": [
        "Step 1: filter the dataframe to only include rows for the specified countries."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "KYaQ1FOg3Xgd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area Code</th>\n",
              "      <th>Area</th>\n",
              "      <th>Item Code</th>\n",
              "      <th>Item</th>\n",
              "      <th>Element Code</th>\n",
              "      <th>Element</th>\n",
              "      <th>Unit</th>\n",
              "      <th>Y1961</th>\n",
              "      <th>Y1961F</th>\n",
              "      <th>Y1962</th>\n",
              "      <th>...</th>\n",
              "      <th>Y2015</th>\n",
              "      <th>Y2015F</th>\n",
              "      <th>Y2016</th>\n",
              "      <th>Y2016F</th>\n",
              "      <th>Y2017</th>\n",
              "      <th>Y2017F</th>\n",
              "      <th>Y2018</th>\n",
              "      <th>Y2018F</th>\n",
              "      <th>Y2019</th>\n",
              "      <th>Y2019F</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2610</th>\n",
              "      <td>100</td>\n",
              "      <td>India</td>\n",
              "      <td>711</td>\n",
              "      <td>Anise, badian, fennel, coriander</td>\n",
              "      <td>5312</td>\n",
              "      <td>Area harvested</td>\n",
              "      <td>ha</td>\n",
              "      <td>90000.0</td>\n",
              "      <td>F</td>\n",
              "      <td>90000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>683055.0</td>\n",
              "      <td>Im</td>\n",
              "      <td>884000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1546000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1564000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1587000.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2611</th>\n",
              "      <td>100</td>\n",
              "      <td>India</td>\n",
              "      <td>711</td>\n",
              "      <td>Anise, badian, fennel, coriander</td>\n",
              "      <td>5419</td>\n",
              "      <td>Yield</td>\n",
              "      <td>hg/ha</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>7993.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>7149.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>9890.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>9610.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>9124.0</td>\n",
              "      <td>Fc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2612</th>\n",
              "      <td>100</td>\n",
              "      <td>India</td>\n",
              "      <td>711</td>\n",
              "      <td>Anise, badian, fennel, coriander</td>\n",
              "      <td>5510</td>\n",
              "      <td>Production</td>\n",
              "      <td>tonnes</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>F</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>546000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>632000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1529000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1503000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1448000.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2613</th>\n",
              "      <td>100</td>\n",
              "      <td>India</td>\n",
              "      <td>515</td>\n",
              "      <td>Apples</td>\n",
              "      <td>5312</td>\n",
              "      <td>Area harvested</td>\n",
              "      <td>ha</td>\n",
              "      <td>44500.0</td>\n",
              "      <td>F</td>\n",
              "      <td>38500.0</td>\n",
              "      <td>...</td>\n",
              "      <td>319000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>277000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>305000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>301000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>308000.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2614</th>\n",
              "      <td>100</td>\n",
              "      <td>India</td>\n",
              "      <td>515</td>\n",
              "      <td>Apples</td>\n",
              "      <td>5419</td>\n",
              "      <td>Yield</td>\n",
              "      <td>hg/ha</td>\n",
              "      <td>41573.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>41558.0</td>\n",
              "      <td>...</td>\n",
              "      <td>66897.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>91011.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>74262.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>77309.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>75195.0</td>\n",
              "      <td>Fc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8765</th>\n",
              "      <td>223</td>\n",
              "      <td>Turkey</td>\n",
              "      <td>1729</td>\n",
              "      <td>Treenuts, Total</td>\n",
              "      <td>5419</td>\n",
              "      <td>Yield</td>\n",
              "      <td>hg/ha</td>\n",
              "      <td>6935.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>8080.0</td>\n",
              "      <td>...</td>\n",
              "      <td>17743.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>10118.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>11865.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>8898.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>10197.0</td>\n",
              "      <td>Fc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8766</th>\n",
              "      <td>223</td>\n",
              "      <td>Turkey</td>\n",
              "      <td>1729</td>\n",
              "      <td>Treenuts, Total</td>\n",
              "      <td>5510</td>\n",
              "      <td>Production</td>\n",
              "      <td>tonnes</td>\n",
              "      <td>227780.0</td>\n",
              "      <td>A</td>\n",
              "      <td>272310.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1126505.0</td>\n",
              "      <td>A</td>\n",
              "      <td>937521.0</td>\n",
              "      <td>A</td>\n",
              "      <td>1118704.0</td>\n",
              "      <td>A</td>\n",
              "      <td>1136407.0</td>\n",
              "      <td>A</td>\n",
              "      <td>1311555.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8767</th>\n",
              "      <td>223</td>\n",
              "      <td>Turkey</td>\n",
              "      <td>1735</td>\n",
              "      <td>Vegetables Primary</td>\n",
              "      <td>5312</td>\n",
              "      <td>Area harvested</td>\n",
              "      <td>ha</td>\n",
              "      <td>318710.0</td>\n",
              "      <td>A</td>\n",
              "      <td>316735.0</td>\n",
              "      <td>...</td>\n",
              "      <td>688476.0</td>\n",
              "      <td>A</td>\n",
              "      <td>718405.0</td>\n",
              "      <td>A</td>\n",
              "      <td>742198.0</td>\n",
              "      <td>A</td>\n",
              "      <td>727822.0</td>\n",
              "      <td>A</td>\n",
              "      <td>747417.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8768</th>\n",
              "      <td>223</td>\n",
              "      <td>Turkey</td>\n",
              "      <td>1735</td>\n",
              "      <td>Vegetables Primary</td>\n",
              "      <td>5419</td>\n",
              "      <td>Yield</td>\n",
              "      <td>hg/ha</td>\n",
              "      <td>118770.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>122731.0</td>\n",
              "      <td>...</td>\n",
              "      <td>344183.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>339939.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>335806.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>331537.0</td>\n",
              "      <td>Fc</td>\n",
              "      <td>339021.0</td>\n",
              "      <td>Fc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8769</th>\n",
              "      <td>223</td>\n",
              "      <td>Turkey</td>\n",
              "      <td>1735</td>\n",
              "      <td>Vegetables Primary</td>\n",
              "      <td>5510</td>\n",
              "      <td>Production</td>\n",
              "      <td>tonnes</td>\n",
              "      <td>3785315.0</td>\n",
              "      <td>A</td>\n",
              "      <td>3887315.0</td>\n",
              "      <td>...</td>\n",
              "      <td>23696207.0</td>\n",
              "      <td>A</td>\n",
              "      <td>24421408.0</td>\n",
              "      <td>A</td>\n",
              "      <td>24923427.0</td>\n",
              "      <td>A</td>\n",
              "      <td>24130023.0</td>\n",
              "      <td>A</td>\n",
              "      <td>25338974.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>897 rows × 125 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Area Code    Area  Item Code                              Item  \\\n",
              "2610        100   India        711  Anise, badian, fennel, coriander   \n",
              "2611        100   India        711  Anise, badian, fennel, coriander   \n",
              "2612        100   India        711  Anise, badian, fennel, coriander   \n",
              "2613        100   India        515                            Apples   \n",
              "2614        100   India        515                            Apples   \n",
              "...         ...     ...        ...                               ...   \n",
              "8765        223  Turkey       1729                   Treenuts, Total   \n",
              "8766        223  Turkey       1729                   Treenuts, Total   \n",
              "8767        223  Turkey       1735                Vegetables Primary   \n",
              "8768        223  Turkey       1735                Vegetables Primary   \n",
              "8769        223  Turkey       1735                Vegetables Primary   \n",
              "\n",
              "      Element Code         Element    Unit      Y1961 Y1961F      Y1962  ...  \\\n",
              "2610          5312  Area harvested      ha    90000.0      F    90000.0  ...   \n",
              "2611          5419           Yield   hg/ha     5000.0     Fc     5000.0  ...   \n",
              "2612          5510      Production  tonnes    45000.0      F    45000.0  ...   \n",
              "2613          5312  Area harvested      ha    44500.0      F    38500.0  ...   \n",
              "2614          5419           Yield   hg/ha    41573.0     Fc    41558.0  ...   \n",
              "...            ...             ...     ...        ...    ...        ...  ...   \n",
              "8765          5419           Yield   hg/ha     6935.0     Fc     8080.0  ...   \n",
              "8766          5510      Production  tonnes   227780.0      A   272310.0  ...   \n",
              "8767          5312  Area harvested      ha   318710.0      A   316735.0  ...   \n",
              "8768          5419           Yield   hg/ha   118770.0     Fc   122731.0  ...   \n",
              "8769          5510      Production  tonnes  3785315.0      A  3887315.0  ...   \n",
              "\n",
              "           Y2015  Y2015F       Y2016  Y2016F       Y2017  Y2017F       Y2018  \\\n",
              "2610    683055.0      Im    884000.0     NaN   1546000.0     NaN   1564000.0   \n",
              "2611      7993.0      Fc      7149.0      Fc      9890.0      Fc      9610.0   \n",
              "2612    546000.0     NaN    632000.0     NaN   1529000.0     NaN   1503000.0   \n",
              "2613    319000.0     NaN    277000.0     NaN    305000.0     NaN    301000.0   \n",
              "2614     66897.0      Fc     91011.0      Fc     74262.0      Fc     77309.0   \n",
              "...          ...     ...         ...     ...         ...     ...         ...   \n",
              "8765     17743.0      Fc     10118.0      Fc     11865.0      Fc      8898.0   \n",
              "8766   1126505.0       A    937521.0       A   1118704.0       A   1136407.0   \n",
              "8767    688476.0       A    718405.0       A    742198.0       A    727822.0   \n",
              "8768    344183.0      Fc    339939.0      Fc    335806.0      Fc    331537.0   \n",
              "8769  23696207.0       A  24421408.0       A  24923427.0       A  24130023.0   \n",
              "\n",
              "      Y2018F       Y2019  Y2019F  \n",
              "2610     NaN   1587000.0     NaN  \n",
              "2611      Fc      9124.0      Fc  \n",
              "2612     NaN   1448000.0     NaN  \n",
              "2613     NaN    308000.0     NaN  \n",
              "2614      Fc     75195.0      Fc  \n",
              "...      ...         ...     ...  \n",
              "8765      Fc     10197.0      Fc  \n",
              "8766       A   1311555.0       A  \n",
              "8767       A    747417.0       A  \n",
              "8768      Fc    339021.0      Fc  \n",
              "8769       A  25338974.0       A  \n",
              "\n",
              "[897 rows x 125 columns]"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "specified_countries = ['India', 'Japan', 'Iran', 'Turkey', 'China (mainland)']\n",
        "sel_prod = prod[prod['Area'].isin(specified_countries)]\n",
        "sel_prod"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HP5EPGKo3Xgd"
      },
      "source": [
        "Step 2: filter the columns to only include Area, Item, Element, and those matching the pattern 'YXXXX'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "7lSt84Vy3Xgd"
      },
      "outputs": [],
      "source": [
        "year_cols = [col for col in sel_prod.columns if (col.startswith('Y') and len(col) == 5)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "X_7GZ4kR3Xgd"
      },
      "outputs": [],
      "source": [
        "sel_prod = sel_prod[['Area', 'Item', 'Element'] + year_cols]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "kSx8epTS3Xgd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Item</th>\n",
              "      <th>Element</th>\n",
              "      <th>Y1961</th>\n",
              "      <th>Y1962</th>\n",
              "      <th>Y1963</th>\n",
              "      <th>Y1964</th>\n",
              "      <th>Y1965</th>\n",
              "      <th>Y1966</th>\n",
              "      <th>Y1967</th>\n",
              "      <th>...</th>\n",
              "      <th>Y2010</th>\n",
              "      <th>Y2011</th>\n",
              "      <th>Y2012</th>\n",
              "      <th>Y2013</th>\n",
              "      <th>Y2014</th>\n",
              "      <th>Y2015</th>\n",
              "      <th>Y2016</th>\n",
              "      <th>Y2017</th>\n",
              "      <th>Y2018</th>\n",
              "      <th>Y2019</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2610</th>\n",
              "      <td>India</td>\n",
              "      <td>Anise, badian, fennel, coriander</td>\n",
              "      <td>Area harvested</td>\n",
              "      <td>90000.0</td>\n",
              "      <td>90000.0</td>\n",
              "      <td>100000.0</td>\n",
              "      <td>100000.0</td>\n",
              "      <td>100000.0</td>\n",
              "      <td>97000.0</td>\n",
              "      <td>97000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>569600.0</td>\n",
              "      <td>693534.0</td>\n",
              "      <td>693700.0</td>\n",
              "      <td>696692.0</td>\n",
              "      <td>738353.0</td>\n",
              "      <td>683055.0</td>\n",
              "      <td>884000.0</td>\n",
              "      <td>1546000.0</td>\n",
              "      <td>1564000.0</td>\n",
              "      <td>1587000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2611</th>\n",
              "      <td>India</td>\n",
              "      <td>Anise, badian, fennel, coriander</td>\n",
              "      <td>Yield</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>4948.0</td>\n",
              "      <td>4948.0</td>\n",
              "      <td>...</td>\n",
              "      <td>7367.0</td>\n",
              "      <td>7747.0</td>\n",
              "      <td>7746.0</td>\n",
              "      <td>7840.0</td>\n",
              "      <td>7909.0</td>\n",
              "      <td>7993.0</td>\n",
              "      <td>7149.0</td>\n",
              "      <td>9890.0</td>\n",
              "      <td>9610.0</td>\n",
              "      <td>9124.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2612</th>\n",
              "      <td>India</td>\n",
              "      <td>Anise, badian, fennel, coriander</td>\n",
              "      <td>Production</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>45000.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>50000.0</td>\n",
              "      <td>48000.0</td>\n",
              "      <td>48000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>419600.0</td>\n",
              "      <td>537277.0</td>\n",
              "      <td>537330.0</td>\n",
              "      <td>546173.0</td>\n",
              "      <td>584000.0</td>\n",
              "      <td>546000.0</td>\n",
              "      <td>632000.0</td>\n",
              "      <td>1529000.0</td>\n",
              "      <td>1503000.0</td>\n",
              "      <td>1448000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2613</th>\n",
              "      <td>India</td>\n",
              "      <td>Apples</td>\n",
              "      <td>Area harvested</td>\n",
              "      <td>44500.0</td>\n",
              "      <td>38500.0</td>\n",
              "      <td>33700.0</td>\n",
              "      <td>26500.0</td>\n",
              "      <td>21700.0</td>\n",
              "      <td>23500.0</td>\n",
              "      <td>30600.0</td>\n",
              "      <td>...</td>\n",
              "      <td>282900.0</td>\n",
              "      <td>289100.0</td>\n",
              "      <td>321900.0</td>\n",
              "      <td>312000.0</td>\n",
              "      <td>313040.0</td>\n",
              "      <td>319000.0</td>\n",
              "      <td>277000.0</td>\n",
              "      <td>305000.0</td>\n",
              "      <td>301000.0</td>\n",
              "      <td>308000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2614</th>\n",
              "      <td>India</td>\n",
              "      <td>Apples</td>\n",
              "      <td>Yield</td>\n",
              "      <td>41573.0</td>\n",
              "      <td>41558.0</td>\n",
              "      <td>41543.0</td>\n",
              "      <td>41509.0</td>\n",
              "      <td>41475.0</td>\n",
              "      <td>41489.0</td>\n",
              "      <td>41503.0</td>\n",
              "      <td>...</td>\n",
              "      <td>62821.0</td>\n",
              "      <td>100000.0</td>\n",
              "      <td>68450.0</td>\n",
              "      <td>61378.0</td>\n",
              "      <td>79788.0</td>\n",
              "      <td>66897.0</td>\n",
              "      <td>91011.0</td>\n",
              "      <td>74262.0</td>\n",
              "      <td>77309.0</td>\n",
              "      <td>75195.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8765</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>Treenuts, Total</td>\n",
              "      <td>Yield</td>\n",
              "      <td>6935.0</td>\n",
              "      <td>8080.0</td>\n",
              "      <td>6837.0</td>\n",
              "      <td>9836.0</td>\n",
              "      <td>5754.0</td>\n",
              "      <td>9213.0</td>\n",
              "      <td>6391.0</td>\n",
              "      <td>...</td>\n",
              "      <td>16450.0</td>\n",
              "      <td>13676.0</td>\n",
              "      <td>18066.0</td>\n",
              "      <td>16407.0</td>\n",
              "      <td>13789.0</td>\n",
              "      <td>17743.0</td>\n",
              "      <td>10118.0</td>\n",
              "      <td>11865.0</td>\n",
              "      <td>8898.0</td>\n",
              "      <td>10197.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8766</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>Treenuts, Total</td>\n",
              "      <td>Production</td>\n",
              "      <td>227780.0</td>\n",
              "      <td>272310.0</td>\n",
              "      <td>232590.0</td>\n",
              "      <td>344415.0</td>\n",
              "      <td>204670.0</td>\n",
              "      <td>331900.0</td>\n",
              "      <td>233800.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1023186.0</td>\n",
              "      <td>857854.0</td>\n",
              "      <td>1153854.0</td>\n",
              "      <td>995221.0</td>\n",
              "      <td>850484.0</td>\n",
              "      <td>1126505.0</td>\n",
              "      <td>937521.0</td>\n",
              "      <td>1118704.0</td>\n",
              "      <td>1136407.0</td>\n",
              "      <td>1311555.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8767</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>Vegetables Primary</td>\n",
              "      <td>Area harvested</td>\n",
              "      <td>318710.0</td>\n",
              "      <td>316735.0</td>\n",
              "      <td>329450.0</td>\n",
              "      <td>343860.0</td>\n",
              "      <td>341070.0</td>\n",
              "      <td>354635.0</td>\n",
              "      <td>353450.0</td>\n",
              "      <td>...</td>\n",
              "      <td>656866.0</td>\n",
              "      <td>668519.0</td>\n",
              "      <td>688339.0</td>\n",
              "      <td>674210.0</td>\n",
              "      <td>666577.0</td>\n",
              "      <td>688476.0</td>\n",
              "      <td>718405.0</td>\n",
              "      <td>742198.0</td>\n",
              "      <td>727822.0</td>\n",
              "      <td>747417.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8768</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>Vegetables Primary</td>\n",
              "      <td>Yield</td>\n",
              "      <td>118770.0</td>\n",
              "      <td>122731.0</td>\n",
              "      <td>124784.0</td>\n",
              "      <td>123632.0</td>\n",
              "      <td>126244.0</td>\n",
              "      <td>118046.0</td>\n",
              "      <td>124751.0</td>\n",
              "      <td>...</td>\n",
              "      <td>314461.0</td>\n",
              "      <td>328767.0</td>\n",
              "      <td>320240.0</td>\n",
              "      <td>338086.0</td>\n",
              "      <td>340307.0</td>\n",
              "      <td>344183.0</td>\n",
              "      <td>339939.0</td>\n",
              "      <td>335806.0</td>\n",
              "      <td>331537.0</td>\n",
              "      <td>339021.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8769</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>Vegetables Primary</td>\n",
              "      <td>Production</td>\n",
              "      <td>3785315.0</td>\n",
              "      <td>3887315.0</td>\n",
              "      <td>4111015.0</td>\n",
              "      <td>4251215.0</td>\n",
              "      <td>4305815.0</td>\n",
              "      <td>4186315.0</td>\n",
              "      <td>4409315.0</td>\n",
              "      <td>...</td>\n",
              "      <td>20655850.0</td>\n",
              "      <td>21978709.0</td>\n",
              "      <td>22043387.0</td>\n",
              "      <td>22794099.0</td>\n",
              "      <td>22684068.0</td>\n",
              "      <td>23696207.0</td>\n",
              "      <td>24421408.0</td>\n",
              "      <td>24923427.0</td>\n",
              "      <td>24130023.0</td>\n",
              "      <td>25338974.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>897 rows × 62 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Area                              Item         Element      Y1961  \\\n",
              "2610   India  Anise, badian, fennel, coriander  Area harvested    90000.0   \n",
              "2611   India  Anise, badian, fennel, coriander           Yield     5000.0   \n",
              "2612   India  Anise, badian, fennel, coriander      Production    45000.0   \n",
              "2613   India                            Apples  Area harvested    44500.0   \n",
              "2614   India                            Apples           Yield    41573.0   \n",
              "...      ...                               ...             ...        ...   \n",
              "8765  Turkey                   Treenuts, Total           Yield     6935.0   \n",
              "8766  Turkey                   Treenuts, Total      Production   227780.0   \n",
              "8767  Turkey                Vegetables Primary  Area harvested   318710.0   \n",
              "8768  Turkey                Vegetables Primary           Yield   118770.0   \n",
              "8769  Turkey                Vegetables Primary      Production  3785315.0   \n",
              "\n",
              "          Y1962      Y1963      Y1964      Y1965      Y1966      Y1967  ...  \\\n",
              "2610    90000.0   100000.0   100000.0   100000.0    97000.0    97000.0  ...   \n",
              "2611     5000.0     5000.0     5000.0     5000.0     4948.0     4948.0  ...   \n",
              "2612    45000.0    50000.0    50000.0    50000.0    48000.0    48000.0  ...   \n",
              "2613    38500.0    33700.0    26500.0    21700.0    23500.0    30600.0  ...   \n",
              "2614    41558.0    41543.0    41509.0    41475.0    41489.0    41503.0  ...   \n",
              "...         ...        ...        ...        ...        ...        ...  ...   \n",
              "8765     8080.0     6837.0     9836.0     5754.0     9213.0     6391.0  ...   \n",
              "8766   272310.0   232590.0   344415.0   204670.0   331900.0   233800.0  ...   \n",
              "8767   316735.0   329450.0   343860.0   341070.0   354635.0   353450.0  ...   \n",
              "8768   122731.0   124784.0   123632.0   126244.0   118046.0   124751.0  ...   \n",
              "8769  3887315.0  4111015.0  4251215.0  4305815.0  4186315.0  4409315.0  ...   \n",
              "\n",
              "           Y2010       Y2011       Y2012       Y2013       Y2014       Y2015  \\\n",
              "2610    569600.0    693534.0    693700.0    696692.0    738353.0    683055.0   \n",
              "2611      7367.0      7747.0      7746.0      7840.0      7909.0      7993.0   \n",
              "2612    419600.0    537277.0    537330.0    546173.0    584000.0    546000.0   \n",
              "2613    282900.0    289100.0    321900.0    312000.0    313040.0    319000.0   \n",
              "2614     62821.0    100000.0     68450.0     61378.0     79788.0     66897.0   \n",
              "...          ...         ...         ...         ...         ...         ...   \n",
              "8765     16450.0     13676.0     18066.0     16407.0     13789.0     17743.0   \n",
              "8766   1023186.0    857854.0   1153854.0    995221.0    850484.0   1126505.0   \n",
              "8767    656866.0    668519.0    688339.0    674210.0    666577.0    688476.0   \n",
              "8768    314461.0    328767.0    320240.0    338086.0    340307.0    344183.0   \n",
              "8769  20655850.0  21978709.0  22043387.0  22794099.0  22684068.0  23696207.0   \n",
              "\n",
              "           Y2016       Y2017       Y2018       Y2019  \n",
              "2610    884000.0   1546000.0   1564000.0   1587000.0  \n",
              "2611      7149.0      9890.0      9610.0      9124.0  \n",
              "2612    632000.0   1529000.0   1503000.0   1448000.0  \n",
              "2613    277000.0    305000.0    301000.0    308000.0  \n",
              "2614     91011.0     74262.0     77309.0     75195.0  \n",
              "...          ...         ...         ...         ...  \n",
              "8765     10118.0     11865.0      8898.0     10197.0  \n",
              "8766    937521.0   1118704.0   1136407.0   1311555.0  \n",
              "8767    718405.0    742198.0    727822.0    747417.0  \n",
              "8768    339939.0    335806.0    331537.0    339021.0  \n",
              "8769  24421408.0  24923427.0  24130023.0  25338974.0  \n",
              "\n",
              "[897 rows x 62 columns]"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sel_prod"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pRTvvDNl3Xgd"
      },
      "source": [
        "Step 3: filter that dataframe to include only the rows for which Element has the value 'Production' and Item contains 'Cereals, Total'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "BDrNHb1S3Xgd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>Item</th>\n",
              "      <th>Element</th>\n",
              "      <th>Y1961</th>\n",
              "      <th>Y1962</th>\n",
              "      <th>Y1963</th>\n",
              "      <th>Y1964</th>\n",
              "      <th>Y1965</th>\n",
              "      <th>Y1966</th>\n",
              "      <th>Y1967</th>\n",
              "      <th>...</th>\n",
              "      <th>Y2010</th>\n",
              "      <th>Y2011</th>\n",
              "      <th>Y2012</th>\n",
              "      <th>Y2013</th>\n",
              "      <th>Y2014</th>\n",
              "      <th>Y2015</th>\n",
              "      <th>Y2016</th>\n",
              "      <th>Y2017</th>\n",
              "      <th>Y2018</th>\n",
              "      <th>Y2019</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2860</th>\n",
              "      <td>India</td>\n",
              "      <td>Cereals, Total</td>\n",
              "      <td>Production</td>\n",
              "      <td>87376496.0</td>\n",
              "      <td>87257552.0</td>\n",
              "      <td>90373008.0</td>\n",
              "      <td>93706000.0</td>\n",
              "      <td>79699504.0</td>\n",
              "      <td>80137608.0</td>\n",
              "      <td>95453504.0</td>\n",
              "      <td>...</td>\n",
              "      <td>267838308.0</td>\n",
              "      <td>287860000.0</td>\n",
              "      <td>293290000.0</td>\n",
              "      <td>294909510.0</td>\n",
              "      <td>296010000.0</td>\n",
              "      <td>284333000.0</td>\n",
              "      <td>297850000.0</td>\n",
              "      <td>310782330.0</td>\n",
              "      <td>321556400.0</td>\n",
              "      <td>324300640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4150</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Cereals, Total</td>\n",
              "      <td>Production</td>\n",
              "      <td>20318660.0</td>\n",
              "      <td>20635150.0</td>\n",
              "      <td>19470310.0</td>\n",
              "      <td>19079380.0</td>\n",
              "      <td>18925506.0</td>\n",
              "      <td>18915600.0</td>\n",
              "      <td>21023600.0</td>\n",
              "      <td>...</td>\n",
              "      <td>11454614.0</td>\n",
              "      <td>11657492.0</td>\n",
              "      <td>12166481.0</td>\n",
              "      <td>12264625.0</td>\n",
              "      <td>12285001.0</td>\n",
              "      <td>12141378.0</td>\n",
              "      <td>11924607.0</td>\n",
              "      <td>11903422.0</td>\n",
              "      <td>11575093.0</td>\n",
              "      <td>11829777.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8736</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>Cereals, Total</td>\n",
              "      <td>Production</td>\n",
              "      <td>12729100.0</td>\n",
              "      <td>14728000.0</td>\n",
              "      <td>17487100.0</td>\n",
              "      <td>14454700.0</td>\n",
              "      <td>14756700.0</td>\n",
              "      <td>16511200.0</td>\n",
              "      <td>16962300.0</td>\n",
              "      <td>...</td>\n",
              "      <td>32764875.0</td>\n",
              "      <td>35195055.0</td>\n",
              "      <td>33370865.0</td>\n",
              "      <td>37475610.0</td>\n",
              "      <td>32708005.0</td>\n",
              "      <td>38632438.0</td>\n",
              "      <td>35276615.0</td>\n",
              "      <td>36126157.0</td>\n",
              "      <td>34395628.0</td>\n",
              "      <td>34398698.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 62 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Area            Item     Element       Y1961       Y1962       Y1963  \\\n",
              "2860   India  Cereals, Total  Production  87376496.0  87257552.0  90373008.0   \n",
              "4150   Japan  Cereals, Total  Production  20318660.0  20635150.0  19470310.0   \n",
              "8736  Turkey  Cereals, Total  Production  12729100.0  14728000.0  17487100.0   \n",
              "\n",
              "           Y1964       Y1965       Y1966       Y1967  ...        Y2010  \\\n",
              "2860  93706000.0  79699504.0  80137608.0  95453504.0  ...  267838308.0   \n",
              "4150  19079380.0  18925506.0  18915600.0  21023600.0  ...   11454614.0   \n",
              "8736  14454700.0  14756700.0  16511200.0  16962300.0  ...   32764875.0   \n",
              "\n",
              "            Y2011        Y2012        Y2013        Y2014        Y2015  \\\n",
              "2860  287860000.0  293290000.0  294909510.0  296010000.0  284333000.0   \n",
              "4150   11657492.0   12166481.0   12264625.0   12285001.0   12141378.0   \n",
              "8736   35195055.0   33370865.0   37475610.0   32708005.0   38632438.0   \n",
              "\n",
              "            Y2016        Y2017        Y2018        Y2019  \n",
              "2860  297850000.0  310782330.0  321556400.0  324300640.0  \n",
              "4150   11924607.0   11903422.0   11575093.0   11829777.0  \n",
              "8736   35276615.0   36126157.0   34395628.0   34398698.0  \n",
              "\n",
              "[3 rows x 62 columns]"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sel_prod = sel_prod[sel_prod.Element == 'Production'][sel_prod.Item ==  'Cereals, Total']\n",
        "sel_prod"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7O_vVGUv3Xgd"
      },
      "source": [
        "Step 4: Plot the line plot of production values from 1961 to 2019 for each of the 5 countries (each country should be a separate line). Only the 'YXXXX' columns should be used for the line plot values.\n",
        "\n",
        "Note: pandas by default plots by column, so to plot each of the countries as its own line, we need to transpose the dataframe so that each column contains the values for one country."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "p2nsOe0s3Xgd"
      },
      "outputs": [],
      "source": [
        "sel_prod_t = sel_prod.T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "Huiphs203Xgd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>2860</th>\n",
              "      <th>4150</th>\n",
              "      <th>8736</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Area</th>\n",
              "      <td>India</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Turkey</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Item</th>\n",
              "      <td>Cereals, Total</td>\n",
              "      <td>Cereals, Total</td>\n",
              "      <td>Cereals, Total</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Element</th>\n",
              "      <td>Production</td>\n",
              "      <td>Production</td>\n",
              "      <td>Production</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Y1961</th>\n",
              "      <td>87376496.0</td>\n",
              "      <td>20318660.0</td>\n",
              "      <td>12729100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Y1962</th>\n",
              "      <td>87257552.0</td>\n",
              "      <td>20635150.0</td>\n",
              "      <td>14728000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Y2015</th>\n",
              "      <td>284333000.0</td>\n",
              "      <td>12141378.0</td>\n",
              "      <td>38632438.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Y2016</th>\n",
              "      <td>297850000.0</td>\n",
              "      <td>11924607.0</td>\n",
              "      <td>35276615.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Y2017</th>\n",
              "      <td>310782330.0</td>\n",
              "      <td>11903422.0</td>\n",
              "      <td>36126157.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Y2018</th>\n",
              "      <td>321556400.0</td>\n",
              "      <td>11575093.0</td>\n",
              "      <td>34395628.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Y2019</th>\n",
              "      <td>324300640.0</td>\n",
              "      <td>11829777.0</td>\n",
              "      <td>34398698.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>62 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   2860            4150            8736\n",
              "Area              India           Japan          Turkey\n",
              "Item     Cereals, Total  Cereals, Total  Cereals, Total\n",
              "Element      Production      Production      Production\n",
              "Y1961        87376496.0      20318660.0      12729100.0\n",
              "Y1962        87257552.0      20635150.0      14728000.0\n",
              "...                 ...             ...             ...\n",
              "Y2015       284333000.0      12141378.0      38632438.0\n",
              "Y2016       297850000.0      11924607.0      35276615.0\n",
              "Y2017       310782330.0      11903422.0      36126157.0\n",
              "Y2018       321556400.0      11575093.0      34395628.0\n",
              "Y2019       324300640.0      11829777.0      34398698.0\n",
              "\n",
              "[62 rows x 3 columns]"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sel_prod_t"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "rOIfpoJE3Xgd"
      },
      "outputs": [],
      "source": [
        "sel_prod_t.columns = ['India', 'Japan', 'Turkey']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "uTAeQZoK3Xgd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Total Cereals Production from 1961 to 2019 (tonnes)'}>"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sel_prod_t[3:].plot(kind='line', title='Total Cereals Production from 1961 to 2019 (tonnes)')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}